Machine Learning Course with Python

Know how Statistical Modeling relates to Machine Learning

  • 48 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques
Group Discount
HRDF Claimable

Description

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

1950:
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

1952:
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

1990:
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

PREREQUISITES

For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Understand how to use Pandas, a must have package for anyone attempting data analysis in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

Learn Machine Learning

Learn Machine Learning

Machine Learning is basically the application of systems that imbibe Artificial Intelligence concepts in order to provide systems with the ability to automatically learn, perform and improve upon set tasks, without requiring reprogramming or human intervention in any form. It is a concept that is focussed on the development of computer systems and programs that are able to access data on their own, analyse it and then learn themselves, without any human help.

The process of Machine Learning commences with the use of observations of data available in different ways such as direct experience, examples etc. The programs and systems then look for patterns in the data provided and then extrapolate these observed patterns in order to make better decisions in the future, based on all these examples and datasets provided to the computer system or program.

There exist several methods of Machine Learning. These can all be categorized into two categories as follows:

Supervised machine learning algorithms:
These are the algorithms that take the learnings from the past data and apply them to the new data made available by making use of labelled examples in order to predict events that will occur in the future.

  1. A known dataset is fed into the system, which the system is then trained on and learns from.
  2. The learning algorithm that is then derived from this training and learning is then produced in the form of an inferred function that makes predictions.
  3. Such kind of algorithms are able to provide us with results for any new inputs after they have undergone sufficient learning and training

Unsupervised machine learning algorithms: These are the type of algorithms that are made use of when the information required for the training of the system and the algorithm are either not labelled or have not been classified.

  1. Unsupervised learning systems are able to infer a function in order to describe a hidden structure from unlabeled data.
  2. Such systems, while being unable to figure out the correct result, are able to explore the available data and draw inferences from the available datasets in order to describe and identify hidden structures from unlabelled data.

Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness the immense amounts of data generated unknowingly every day and put it to use for key decisions. Big and small data is reshaping technology and business as we know it and will continue to do so (in the near future at least).

The state of Machine Learning in companies and in your daily life

Machine Learning is no more just a mere niche of tech world but is a new field of work and research altogether. Tech experts have been increasingly making use of Machine Learning over the years. Surge pricing at Uber, Walmart product recommendations, Social media feeds displayed by both Facebook and Instagram, Google Maps, detecting financial fraud at financial institutions etc - all these and many more functionalities are now being performed with the help of powerful Machine Learning algorithms, increasingly without human interference.

Every individual is making use of one or the other product of Machine Learning, whether he knows it or not. In such a scenario, learning about Machine Learning is an inevitable step that any professional, especially someone involved in the field of Information Technology and Data Science, must take in order to not become irrelevant.

Some of the benefits of learning Machine Learning include the following:

  1. It reels in better job opportunities:
    According to a report published by Tractica, services driven by Artificial Intelligence were worth $1.9 billion in the year 2016 and this number is expected to rise to the neighbourhood of around $19.9 billion by the end of the year 2025. Machine Learning is the bandwagon that every corporation in the world is hitching its wagon to. With each industry in the world looking at expansion in the domain of Machine Learning and Artificial Intelligence, a knowledge of the same is bound to attract more and brighter career opportunities in the present scenario and in the future as well.

  2. Machine Learning engineers earn a pretty penny:
    The valuation of a Machine Learning expert can be compared to that of a top NFL quarterback prospect. According to a study published by SimplyHired.com, the average income of a machine learning engineer is pegged at $142,000, whereas an experienced machine learning engineer can earn up to $195,752 per year.

  3. Demand for Machine Learning skills is only increasing:
    There exists a huge gap between the demand and the availability of Machine Learning engineers. This skill gap is increasingly being lamented by the Chief Information Officers (CIOs) of some of the biggest corporations around the world. What this means, essentially, is that the demand as well as the pay for professionals with Machine Learning skills is only going to increase in the future.

  4. Most of the industries are shifting to Machine Learning:
    Most industries in operation around the world are dealing with a humongous amount of data that is only increasing every single day. The benefits reaped by a thorough analysis of this data is a fact that companies are fast taking cognizance of. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors.

    At such as time, all industries from the financial services sector to government agencies, the healthcare industry and oil and gas mega corporations to the transportation sector - every industry is a ripe field for work in the field of Machine learning.

Machine learning is a huge field which is expanding day by day due to its diverse and open culture. However, we can assure you that self- learning ML will be effective as long as you are motivated and keep these points in mind

  • You will learn practical skills faster and learn to implement them through hands-on learning more easily than by going through research papers and textbooks or learning to derive proofs. In the real world, you will never actually be required to derive a proof but will be asked to understand and make the best use of available data.

  • You will be building your profile as an ML enthusiast along the way as you keep building on projects. Projects are the best way to attract employers and when we self start, the best way to test our skills is to simply implement them through a project.

Below are the steps you can follow:

  1. Structural Plan: At the outset, you need to create a structured plan on the topics that you must familiarize yourself with first and what can be learnt later.

  2. Prerequisite:  Pick a programming language you are comfortable with and start polishing your mathematical and statistical skills as ML involves dealing with statistical data.

  3. Learning: Start learning according to the plan created in step (1). Refer to various reliable sources online or from books. Understanding the workflow of ML algorithms is a must.

  4. Implementation: We can never truly learn unless we learn how to implement our skills. Try to build projects which use the algorithms you learned. Take datasets from the internet and start solving problems, and take part in online competitions such as Kaggle to learn more.

    What is most important is that you keep on tackling ML problems on a regular basis so as to polish your skills and incorporate out of the box thinking in an otherwise simple solution.

The best recommendation to get started on Machine Learning as a beginner includes a 5 step process, which goes as follows:

  1. Adjust your mindset
    • Take cognizance of what is holding you back from achieving your goals in Machine Learning.
    • Teach yourself to believe that Machine Learning is not as difficult as the hype would like you to believe.
    • Think of Machine Learning as a concept that unravels itself the more you practice it.
    • Find your Machine Learning tribe - look for people who will support and aid you in your journey of learning Machine Learning.
  2. Pick a process that suits you best: Choose a process that is structured and systematic and suits your way of working through problems

  3. Pick a tool: Pick a tool that best suits your level of comfort with the Machine Learning concepts and map these onto your processes.
    • Beginners are recommended to opt for the Weka Workbench
    • Intermediate level learners are recommended the Python Ecosystem
    • Advanced level learners are recommended the R Platform
  4. Practice on Datasets: Choose from a host of available datasets to work on and practice the entire process of data collection and manipulation.
    • Practice your own Machine Learning skills with relatively small, installed in memory datasets
    • Take cognizance of the real world problems connected to the world of Machine Learning
  5. Build your own portfolio:
    • Make use of the knowledge you have gained and demonstrate your skills in the form of a portfolio

The co-founder and CTO of Kaggle held a Quora session last month, in which he answered questions on Machine Learning and Artificial Intelligence, the future of Kaggle as well as data science workflows. The following is a summary of his recommendations for studying Machine Learning in eight steps:

  1. Choose a problem that you are interested in: Starting your Machine Learning journey with a problem that interests you is one of the best ways to stay focussed and motivated throughout your machine learning journey. Solving a problem that you are interested in, will keep you engaged in the world of Machine Learning, instead of picking and learning disconnected and potentially intimidating topics. The criteria for a topic to qualify as a ‘good topic’ include the following:
    • The topic is an area that you are interested in
    • Data regarding the topic is easily available
    • The data can be worked on comfortably on a single machine
  2. Make a quick, hacky and messy end to end solution to the problem: It is very common for students of Machine Learning to get lost or bogged down in the careful pruning of a machine learning algorithm or the implementation of a machining learning algorithm. But it is important to remember that the best way to achieve a solution to your problem is to first form a quick, basic and not so perfect solution to the problem that covers the end-to-end platform and then work on pruning it and moulding it into an ideal solution.

  3. Evolve and work on your initial, basic solution: Now that you have the basic outline of your solution, it is time to work on getting towards the perfect and ideal solution. Try working on the improvement of each component of your basic solution and then measure the impact of this change on the overall solution. Very often, steps like the improvement of data cleaning, acquiring more data as well as improving the efficiency of the preprocessing steps give a higher ROI as compared to the optimization of the Machine Learning models themselves.

  4. Write and share your solution: The best way to understand if you have a good solution to a problem is to write it up and share it in the Machine Learning community, so as to obtain feedback for your fellow Machine Learners. This step not only enables your peer group to look at, evaluate your solution and provide you with feedback but also opens the door for newer and more efficient ideas and solutions to a problem to emerge.

  5. Repeat steps 1 to 4 and apply them to a diverse set of problems: Repeat the above steps over a series of diverse problems, so as to be able to grasp and totally understand different concepts and machine learning models.

  6. Take part in a Kaggle competition and compete seriously (if you haven’t done so already): Putting in your best efforts directed at a problem that thousands of other developers are working on, gives you a boost and makes you work harder in order to outsmart others. This helps you clear several concepts and learn a lot along the way. It also forces you to iterate over a problem, again and again, thus forcing you to come up with an effective and efficient solution to the issue at hand.

  7. Apply machine learning concepts professionally: Application of machine learning concepts professionally helps you really understand the concepts of Machine Learning and takes you a level higher in the field of Machine Learning. Building a professional and personal portfolio as well as deciding upon the type of role you want to work on as a Machine Learning professional is a strong starting point to initiating your journey as a Machine Learning professional. Some valuable opportunities for a machine learning professional include the following:
    • Application of Machine Learning concepts in Production systems
    • Focussing on Machine Learning research and boosting the progress of the systems.
    • Leveraging Machine Learning in exploratory analyses so as to as improve business decisions.
  8. Teach others: Teaching someone else the concepts of Machine Learning will aid you in the solidification of your own Machine Learning concepts like nothing else can. There are a number of ways you can go about teaching Machine Learning to others. Some of these ways include the following:
    • Writing tutorials
    • Penning down blog posts
    • Writing a book
    • Writing a research paper
    • Giving talks on Machine Learning concepts
    • Teaching a class on Machine Learning
    • Sharing code snippets on platforms like GitHub and Kaggle
    • Answering questions on the concepts of Machine Learning on platforms such as Quora, Kaggle etc.
    • In person mentoring and tutoring

In order to thoroughly understand the concepts of Machine Learning and to develop successful Machine Learning projects, it is important to know the following:

  1. Programming languages: To be able to comfortably work with programming languages such as Python, Java, Scala etc is an important prerequisite before you embark upon your Machine Learning journey. A learner of Machine Learning must be able to make use of these programming languages, at least in a basic manner, so as to be able to grasp Machine Learning skills more thoroughly. Knowledge of formatting data formats, processing the data in order to make it compatible with the machine learning algorithm etc are also skills that will come in handy on your journey to mastering Machine Learning concepts and their application in real life.

  2. Database skills: A prior knowledge and experience of working with MySQL as well as relational databases is also a prerequisite to fully gauging and understanding Machine Learning concepts. During the course of their Machine Learning journey, learners will have to make use of data sets obtained from various different data sources simultaneously. Programmers must be able to read data available at different sources, and then convert this data obtained in a format that is readable by as well as compatible with the machine learning framework that they are working on at the moment.

  3. Machine Learning visualization tools: There exist several tools that are available for visualizing the data being used in Machine Learning. A basic knowledge and understanding of some of these tools will turn out to be helpful while you are applying the concepts of Machine Learning in real life.

  4. Knowledge of Machine learning frameworks: Several statistical, as well as mathematical algorithms, are made use of in order to design a Machine Learning model to learn from the input data and come to a prediction for a given data set. Knowledge of one or more of these frameworks including Apache Spark ML, Scala NLP, R, TensorFlow etc. is a prerequisite for a thorough understanding of Machine Learning concepts.

  5. Mathematical skills: Mathematics is at the heart of Machine Learning. This is because it is through these mathematical algorithms and concepts that the data is processed, analyzed and used in order to form a Machine Learning model. The following is a list of some of the mathematical concepts that a student of Machine learning must know in order to understand and implement the machine learning models and concepts successfully:
      1. Optimization
      2. Linear algebra
      3. Calculus of variations
      4. Probability theory
      5. Calculus
      6. Bayesian Modeling
      7. Fitting of a distribution
      8. Probability Distributions
      9. Hypothesis Testing
      10. Regression and Time Series
      11. Mathematical statistics
      12. Statistics and Probability
      13. Differential equations
      14. Graph theory

If you want to your ML project to be executed successfully, we have compiled the steps for the same below:

  1. Gathering data: The most important step is getting the correct data for your project upon which you will be performing your ML magic. The quality and quantity of your data are directly proportional to the performance of your model.

  2. Cleaning and preparing data: Data we have gathered is just raw data and is yet not ready to be injected directly into our model. In this step, we carefully clean the data which means correct the missing data that we may have and secondly prepare the data. Preparing the data means specifically feature engineering. Convert the raw data to the data our model is expecting and finally divide the data into 2 parts: training data and testing data.

  3. Visualize the data: Sometimes this is the final step of the project, to just show the prepared data and find the correlation between the variables. Visualization helps in understanding the kind of data that we have in our hands and help to make a good selection of model accordingly.

  4. Choosing the correct model: After visualizing of data we have a good knowledge that how this data can be harvested and which model or algorithm is best suited to do so. Choosing the correct model significantly determines the performance of your algorithm.

  5. Train and test: We have our prepared data ready to be injected into our chosen model. As in earlier step we have divided our data into training and testing data, we now train our model with the training data and after it is trained, we test its accuracy with the test data in which it wasn’t trained.

  6. Adjust parameters: After finding how accurate your model is, we can fine tune our parameters. One example is changing the number of neurons in a neural network.

After following all these steps, you would have successfully created and executed your machine learning project. Just don’t forget to write a blog on your new findings (if any)!

Algorithms form an integral part of the study of Machine Learning, which is why it is very vital for all learners of Machine Learning to know, understand and ingrain the concepts of Machine Learning algorithms. Here’s how to do that:

  1. List the various Machine Learning algorithms: While each algorithm is unique and important in its own way, it is vital for you to decide and list down some algorithms that you wish to begin your Machine Learning journey with. Begin by making a text or doc file and enlist all the algorithms that you wish to learn. Also make sure that you list the general category that the particular algorithm falls under. This particular activity will also help you build familiarity with the various classes and types of algorithms that are available and prepare you for what lies ahead.

  2. Apply the Machine Learning algorithms that you listed down: Remember that Machine Learning algorithms do not exist in isolation and no matter how much time you spend in learning the theory of Machine Learning algorithms, the best learning comes from the practical application and implementation of Machine Learning algorithms to data sets.Thus, apart from learning the basic concepts and theory of Machine Learning algorithms, it is also important to practice Applied Machine Learning.Start building up an intuition for the various Machine Learning algorithms such as Support Vector Machines, decision trees etc. Build confidence by applying these algorithms to various problems and data sets.

  3. Describe these Machine Learning algorithms: The next logical step to be undertaken in order to gain a better understanding of Machine Learning algorithms is to explore what has already been understood about these algorithms. A thorough analysis and understanding of  Machine Learning algorithms will help you build up a description of these algorithms. Continue adding more and more information to these descriptions as you discover more information through the course of your study of Machine Learning algorithms. This is quite a valuable technique to help you build up a mini algorithm encyclopaedia of your own. 

  4. Implement Machine Learning Algorithms: The implementation of Machine Learning algorithms is the most concrete way of learning the working of an algorithm. By implementing the algorithm yourself, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts. The implementation of algorithms also help you get a feeling about the working of an algorithms as well as understand the mathematical extensions and descriptions of the algorithm.

  5. Experiment on Machine Learning Algorithms: Once you have implemented and understood a Machine Learning algorithm, you are well placed to experiment with an algorithm. You can now use standardized data sets, control variables as well as study the functioning of algorithms in the form of a complex system in itself. Understanding the parameters at play during the working on algorithm is a great way to be able to customize its working in order to suit your needs while working on a problem. Understanding the behaviour of an algorithm also enables you to better scale and adapt an algorithm to suit your problem needs in the future.

The following list offers the top Machine Learning books that are extremely useful for beginners- 

  1. Neural Networks and Deep Learning
  2. Machine Learning Yearning: By Andrew Ng
  3. Natural Language Processing with Python: By Steven Bird, Ewan Klein, and Edward Loper
  4. Statistical inference for data science
  1. Neural Networks and Deep Learning: By Michael Nielsen
      1. This is an online book available for free.
      2. It teaches you all about deep learning, some of the powerful learning algorithms in ML.
      3. It also teaches you about the neural networks from the basic level to the advanced one.
  2. Machine Learning Yearning: By Andrew Ng
      1. It is written by the Coursera co-founder and author of one of the most popular and successful online course on ML - Andrew Ng.
      2. It is still under development and available for free.
      3. Along with the theory and implementation guidance, it also teaches how to plan, organize, and execute your very own ML project.
  3. Natural Language Processing with Python: By Steven Bird, Ewan Klein, and Edward Loper
      1. People interested in the Natural language processing part of ML should definitely read this book for the lucid manner in which it is written and some great well-presented python codes in it.
      2. The readers are provided with well documented datasets and are taught to deal with the linguistic structure of text, unstructured data etc.
  4. Statistical inference for data science: By Brian Caffo
      1. This is a companion book to the Statistical interference class from Coursera.
      2. Written specially for students/professionals who are more inclined towards mathematical and statistical aspect of ML.
      3. This book is also available online and for free.

Below are some of the top free online courses for Machine Learning- 

  1. Intro to Machine Learning - Udacity
  2. Introduction to Machine Learning Problem Framing - Google
  3. Machine Learning Crash Course with TensorFlow APIs - Google
  4. Deep Learning for Self-Driving Cars - MIT
  1. Intro to Machine Learning - Udacity: 10 weeks course. You will learn how to investigate data via an end-to-end process which will teach you to identify and extract useful features, how to select the best algorithm for a specific type of problem and finally the techniques to evaluate the performance of your ML project. Prerequisites for this course are:
    • Basic data structures and control statements
    • Inferential Statistics
    • Descriptive Statistics
  2. Introduction to Machine Learning Problem Framing - Google: 1-hour course. It is part of an initiative by Google to make the ML knowledge as accessible as possible to the world. The course material keeps going to changes to accommodate new innovations in the field. It teaches how to apply hypothesis testing and the scientific method to ML problems among introductory ML theories along with their implementations.
  3. Machine Learning Crash Course with TensorFlow APIs - Google: 15 hours course. It is a deep learning crash course that introduces the use of TensorFlow, one of the most used and efficient libraries for deep learning. There are above 40 exercises to be done by the students which will be reviewed. Prerequisites for this course are:
    • Mastery of intro-level algebra
    • Proficiency in programming basics, and some experience coding in Python
  4. Deep Learning for Self-Driving Cars - MIT: 18 hours course. This class helps in understanding and implementing your deep learning skills through a famous and practical example of Self-driving cars. Students, as well as researchers, can benefit from their course as it gives a practical overview of deep learning methods and their applications. Prerequisites for this course are:
    • Good understanding of standard ML terms and algorithms
    • Proficiency in mathematics and statistics.

Although both machine learning and deep learning are parts of artificial intelligence, there are some differences between them. The main difference between standard machine learning algorithms and deep learning algorithms is that deep learning algorithms are closer to human brains as compared to its counterpart. If a prediction is wrong, then in a standard ML algorithm, an engineer must tune it and make proper adjustments whereas, in deep learning algorithms, it itself fine-tunes the settings if the prediction is wrong making it resemble like a ‘brain’. Some other significant differences between them are as follows:

  1. Data: The amount of data the algorithm has to deal with significantly determines its accuracy and performance. As the scale of data increases, standard machine learning algorithms start performing poorly and deep learning algorithms take precedence. This is due to the fact that to ‘understand’ the data completely deep learning algorithms require a large amount of it which is why they don’t perform well when the scale of data is small where standard machine learning algorithms have the upper hand due to their handcrafted rules.

  2. Feature engineering: Data provided to us is not always suitable and ready to be injected into our model, instead it needs to be preprocessed and feature extractors are to be built to reduce the complexity of the data itself while making patterns and features more visible for the learning phase of the model. These features are applied by experts in machine learning algorithms, which is not the case in deep learning algorithms where the algorithm itself attempts to initially learn low-level features and then gradually move on towards high-level features.

  3. Time: As deep learning algorithms have to learn a huge amount of data, their learning phase, in turn, becomes long as compared to that of standard machine learning algorithms. This is countered in the testing phase where ranks are reversed-deep learning algorithms, now fully trained, take less testing time as compared to their counterparts which due to less training time and data less trained.

  4. Hardware: Deep learning algorithms work on huge chunks of data and perform high computational tasks which in turn require high-end machines as opposed to low-end machines upon which standard machine learning algorithm works without much hassle. This is due to the fact that deep learning algorithms require GPUs for their heavy matrix multiplication calculations.
  • Just do it: The best way to learn Machine Learning through self-study is to learn by just doing it.

  • Don’t fear mistakes: Make as many mistakes as you can, because the more mistakes you make, the more you dive into the fine print of Machine learning and thus, the more you understand the concepts and the more you learn.

  • Avoid this common mistake: One common mistake that learners of Machine Learning through self-study make is to spend the first few months or even the first few years on the mathematics as well as the theory behind the concepts of Machine Learning. Such learners then get frustrated over time, owing to the sheer volume of academic papers and textbooks they read, apart from the formulas and the arcane symbols that they have involved in their course curriculum.

  • What experts recommend: Our recommendation, however, is that unless you wish to do a Ph.D. research and write a paper about these topics, covering all these areas is overkill and a total waste of time, energy and efforts. Simply start with our course pack on Machine Learning and learn only that which is relevant and useful to your application and understanding of Machine Learning. 

Although Python is not a necessity for machine learning, it will certainly make your life easier when dealing with Machine Learning concepts and their applications. Python for Machine Learning has several advantages, including the following:

  • It is one of the most used languages in artificial intelligence and other related areas of scientific progress and study.

  • It contains libraries that are specially designed to suit Machine learning requirements. Some of these specially designed libraries for Machine Learning include NumPy and SciPy, both of which are useful in learning kernel methods as well as for use in linear algebra. 

  • Python is the most preferred language for machine learning owing to the low level of complexity it offers, apart from its ease of readability. It can be very easily understood by and explained to an individual who does not have a background in programming languages.

  • Sometimes, the concepts of Algebra, Calculus etc. that are extensively made use of in Machine Learning are very complex to implement or even understand and take a huge amount of time and effort. A quick implementation of these in Python can make the life of a Machine Learning engineer or professional easy by helping them validate and visualize their ideas.

  • The SciKit Module offered by Python contributes a lot in guiding Machine Learning professionals in the implementation of the concepts to real-world problems.

In conclusion, while Python is not really a necessity for Machine Learning, it surely does make life easier for Machine Learning professionals, while also increasing the efficiency of their work.

As Machine learning is now being used in an ever-expanding array of products, so are the tools which enable a person to become an ML programmer. The most important tool after data in an ML project is the programming language which you will be using to code your ML magic which is why it is imperative that you choose a programming language that is best and hottest in the market. Below is a checklist to filter out the best programming language for you:

  1. Does the language have a diverse, and large open-source community?
  2. Are there enough libraries supported for the language for data manipulation?
  3. Is it easy to code in for both beginners and have advanced features for experts?

And we could find only one programming language fitting all the criteria and it is, without any doubt, Python. Python satisfies all the above conditions to grab the #1 spot for programming in machine learning.

  1. Python has great readability and less complexity.
  2. Numerous and fast ML-based packages: SciPy, scikit-learn, NumPy, pandas, matplotlib etc.
  3. Fast and easy deployment.
  4. In Python, we can play with the data without much expertise in the field.

There are some other programming languages in the market as well: R, MATLAB etc. but they have their own constraints.

  • MATLAB provides great libraries and packages however its execution time is very slow due to which it can’t be used in deployment.
  • R is open source, and free software however it requires learning to code in it comfortably as it has predefined functions for some predefined data. We have to always prepare data in a way that is acceptable to the functions of R.

No, an intimate understanding of kernel methods is not a prerequisite for the Machine Model. Just like any other concept you learn, the required depth of understanding of a particular concept is related to the practical application it has to you. The more complex your application of the concept and its learnings, the more in-depth you have to read and learn the concept. 

Gaining a very deep understanding of Machine Learning concepts is beyond the scope of this course. This course aims to acquaint you with the basic concepts and understanding of Machine Learning and looks at equipping you with the basic tools and techniques required for you to dive into the practical applications of Machine Learning.

A more deep understanding of Machine Learning that includes an intimate knowledge of kernel methods and the like, requires a substantial amount of investment in the form of time, and is ideally carries out in a more academic setting as compared to a self paced, at-home learning course, or very intensive self study at the very least.

No, you do not need to be a Python Programmer in order for you to learn Machine Learning. You can learn the concepts of Machine Learning without being a Python Programmer or while having very little and limited knowledge of Python programming. However, the implementation of the theoretical concepts of Machine Learning can come only by the knowledge of Python programming. Even some basic knowledge of the same is sufficient.

Machine Learning Algorithms

"Show me who your friends are and I’ll tell you who you are"

The above phrase perfectly sums up the concept behind the K Nearest Neighbours algorithm. And surprisingly, that is all there is to it. The K Nearest Neighbours algorithm is a rather simplistic and uncomplicated Machine Learning algorithm. Given a totally multiclass dataset to be worked on, with the goal of predicting the class of a given data point, we can make use of the K Nearest Neighbour algorithm.

  • The primary requirement of the nearest neighbour classification is the definition of a pre-defined number, which will be stored as the value of ‘k’. This number, k, defines the number of training samples that are closest in distance to a new data point that is to be classified. 

  • The label that will be assigned to this new data point, will then, be one that has already been assigned to and defined by these neighbours. 

  • K-nearest neighbor classifiers possess a fixed user-defined constant for the number of neighbors which have to be determined.

  • These algorithms work on the concept of radius based classification. The concept behind the radius based classification is that depending on the density of the neighbouring data points,all the samples are identified and classified under and inside a fixed radius. This fixed radius is a metric measure of the distances and is most popularly, the Euclidean distance between the points.

  • All these methods based on the classification of the neighbours are also known as the non-generalizing Machine Learning methods. This is majorly owing to the fact that these methods ‘remember’ all the training data that was fed into it, instead of acting on them.

  • Classification is then performed as a result of a majority vote conducted among the nearest neighbours of the unknown sample.

The K Nearest Neighbour algorithm is the simplest of all machine learning algorithms. However, in spite of its simplicity, the algorithm has proven to be very successful and useful in the solution to a huge number of regression as well as classification problems, an example of which includes character recognition as well as image analysis.

The answer to this question depends upon what you intend to do with Machine Learning. 

  • In case you simply wish to use the existing Machine Learning algorithms, then you can study Machine Learning without knowing any classic algorithms. There exist several courses on the internet that offer a lot of knowledge on Machine Learning, without requiring any knowledge of algorithms.

  • In case you wish to innovate with the help of Machine Learning, having some basic knowledge of the workings and uses of algorithms is a critical prerequisite. Since you will basically be involved in the adaptation of a new algorithm or even designing a new one, you need the tools and the knowledge that is necessary in order to adapt, design and innovate using Machine Learning. The knowledge regarding the correctness of an algorithm, how much time an algorithm takes, its complexity, costs involved etc are all concepts that you will get familiar with, only after you have sufficient knowledge of the algorithms themselves. 
  • Supervised machine learning algorithms: These are the algorithms that take the learnings from the past data and apply them to the new data made available by making use of labelled examples in order to predict events that will occur in the future. 
    • A known dataset is fed into the system, on which the system is then trained on and learns from. 
    • The learning algorithm that is then derived from this training and learning is then produced in the form of an inferred function that makes predictions. 
    • Such kind of algorithms are able to provide us with results for any new inputs after they have undergone sufficient learning and training.
  • Unsupervised machine learning algorithms: These are the type of algorithms that are made use of when the information required for the training of the system and the algorithm is either not labeled or has not been classified. 
    • Unsupervised learning systems are able to infer a function in order to describe a hidden structure from unlabeled data.
    • Such systems, while being unable to figure out the correct result, are able to explore the available data and draw inferences from the available datasets in order to describe and identify hidden structures from unlabelled data.
  • Reinforcement learning: Reinforcement learning is an area of Machine Learning that deals with the taking of suitable steps in order to maximize the outcomes or rewards in particular situations. Reinforcement learning differs from supervised learning in that in Supervised learning, the training data possesses the answer key within it so that the model being trained is trained with the correct answer itself. In reinforcement learning, however, the agent of reinforcement decides what is to be done in order to perform a given task. In the absence of such a training dataset, the model is bound to learn everything from experience. 
    • The input into the model must be from an initial state from which the model must start
    • There must be as many possible outputs as possible as there exist a variety of solutions in order to solve a particular problem.
    • The training of the model in a reinforcement learning model is supposed to be based on the input fed into it. The model is to return a particular output state. Depending upon the state that the model returns, the user will then decide whether to reward or punish the model on the basis of this output.
    • The best solution to the problem is decided upon based on the maximum reward obtained and received by the model.

Machine Learning Algorithms can be classified basically into the following 3 types - 

  • Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbours 
  • Unsupervised Learning: Apriori, K-Means, Principal Component Analysis (PCA) 
  • Ensemble Learning: Bagging, Boosting
  • Supervised Learning: Using categorically classified historical data to learn the mapping function from the input variables (X) to the output variable (Y). Examples of such include
    1. Linear Regression -  The relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx

    2. Logistic Regression - Logistic Regression is just like the linear regression model; the only difference is the outcome of the regression is probabilistic, rather than exact values. A Transformation function is then applied to force this probability into a binary classification. 

    3. CART - Classification and Regression Trees (CART) is an implementation of Decision Trees. This algorithm charts the possibility of each outcome and predicts the result on the basis of defined nodes and branches. At each non-terminal node is a single input variable (x). The splitting point on that node depicts the various outcomes that can happen to that variable, and the following leaf node represents the output variable (y).

    4. Naïve Bayes - This algorithm predicts the possibility of an outcome happening, given the basic value of some other variable. It works exactly on the principle of the Bayes theorem, and is considered “naive” as it makes the assumption that all variables are independent in nature. 

    5. K-Nearest Neighbours - This algorithm charts the entire data set given, and after assigning a pre-defined value of “k” to find out the outcome for a given value of the variable, it collects “k nearest instances” of the value in the dataset and then either averages them to produce the output (for a regression model) or finds the mode of these averages (for most frequent class problem). 
  • Unsupervised Learning: In these type of problems, only the input variables are given and not the output ones. Thus, the underlying structure of the given data sets is analysed to reveal possible associations and clusters. Examples of such algorithms include the following -
    1. Apriori -  This algorithm is used in various databases containing transactions to identify frequent associations or instances of two items occurring together, then such associations are used to predict further relationships.

    2. K-Means -  This algorithm groups similar data into clusters, and them associates each data point in the cluster to an “assumed” centroid of the cluster. By performing iterations of the steps to ensure the distance between the data point and the centroid is the closest, the real centroid is arrived for each cluster.

    3. PCA -  Principal Component Analysis (PCA) makes the data space easier to visualize, by reducing the number of variables. It does this by mapping the maximum variances of each point onto a new coordinate system, with axes corresponding to the principal components chosen. The basic principle of orthogonality ensures that each pair of components is unrelated to each other.
  • Ensemble Learning : Groups or ensembles of learners are more likely to perform better than singular learners. By building on this premise, these type of algorithms combine the results of each learner and then analyse them as a whole to obtain a fairly accurate representation of the actual outcome. Examples of such algorithms include the following 
    1. Bagging - This algorithm is used to generate multiple datasets (based on the original one), then model the same algorithm on each to produce different outputs, which can then be compiled and performed upon to obtain the real outcome.

    2. Boosting - This algorithm is similar to the above one, but it works sequentially instead of the parallel nature of bagging. Thus, each
      new dataset is created by learning from the previous one’s errors and miscalculations. 

On the whole, there are many ML algorithms, all of which are broadly categorized like the following:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Semi-supervised  Learning
  4. Reinforcement Learning

Our aim is to find a way to predict output B when provided with sets of inputs A.

  • Supervised Learning - In supervised learning, we know a question and we also know the answer, our aim is to find a way/pattern so that we reach from the question to our desired answer. Humans experts provide the computer with inputs (A) and the correct output (B) is also provided. The machine is, therefore, expected to learn the pattern which would lead us from the input to our desired outputs. Below are some of the popular supervised learning algorithms:
    • Linear Regression: It aims to find a modeling relationship between independent variables(A) and a dependent variable (B) by using line equation. It is used for predictive analysis.

    • Random Forest: Decision trees, by design are vulnerable to high variance. To reduce variance, we limit the rows (features) calculated at each split point of the tree to make them different. It also removes overfitting as compared to decision trees output.
  • Unsupervised Learning - Here, we don’t know the value of B but we know A. So our aim becomes to group this collection of unsorted information according to patterns, and similarities in them without any prior training. Most popular unsupervised learning algorithms are:
    • K-Means: It aims to find groups in the data provided by working iteratively to assign all the data entries to one of these groups by using features (A) provided. K here defines the number of these groups.
  • Semi-supervised  Learning - In the above 2 learnings,  either all the data is labeled or none of it is. This learning falls in between the two. As in real life labeling of data is expensive, labels for all the data is not possible and only some of it is labeled. For such cases, this learning is best as it exploits the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. The most popular algorithm which uses this learning is Co-training which is used for text mining in search engines.

  • Reinforcement Learning - Rather than randomly trying to find our paths from A to B, we try to guide the machine step by step or state by state by giving a reward each time it does the right thing. Greedy approach is used to take the step (decision) that will yield the best result. To take decisions, it uses 2 approaches -
    • Policy learning - It tells the agent exactly what to do at each state.
    • Q-Learning / Value Functions - Our algorithm is provided with a framework to make its own decisions rather than we explicitly telling it at each step.

The simplest of machine learning algorithms solve the simplest of ML problems (simple recognition). We have selected the algorithm based on the following criteria:

  • Easy to understand.
  • Easy to implement and understand the underlying principles.
  • Takes less time and resources to train and test the data as compared to high-level algorithms.

Now we introduce you to the algorithm itself which is a stepping stone in your journey towards mastery in ML: k-nearest neighbor algorithm. Below are some of the reasons why we chose kNN as the simplest ML algorithm and is used extensively for solving some of the basic, but important, real-life problems:

  • One of the simplest supervised learning algorithm and best suited for beginners is the k-nearest neighbor algorithm.
  • It is a classification algorithm though can be used for regression as well.
  • It classifies based on the similarity measure and is non-parametric.
  • Data set used for the training phase is labeled data (supervised learning) and the aim of the algorithm becomes predicting a class for an object based on its k nearest surroundings where k defines the number of neighbors.
  • Some practical and real-life examples where KNN is used are:
    • When searching in documents containing similar topics.
    • Used to detect patterns in credit card usage.
    • Vehicular number plate recognition.

Due to ML being the hottest prospect in the tech world right now, it has loads of tools, algorithms, and models that you can choose from. But one has to keep in mind certain things while selecting the algorithm which becomes the backbone of your project.

  • Understanding your data: First and foremost comes your data upon which you have to apply your algorithm, so to find the correct algorithm you first have to understand your data.
    • Visualize your data by plotting graphs.
    • Try to find correlation among the data which indicate strong relationships.
    • Your data is not always perfect, there can be missing data or bad data as well which can be sensitive to your model. Deal with this and clean your data.
    • Prepare your data by feature engineering to make your data ready to be injected into your model.
  • Get the intuition about the task: Many times we don’t understand the underlying aim of the task, and that’s why ML is needed to solve the problem in the first place. After understanding that, you need to see which kind of learning will help your model complete the task at hand. There are 4 types of learning in general:
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
  • Understand your constraints: If we don’t apply constraints to our planning while choosing algorithms, we will go ahead and choose the best of the best tools and algorithms out there. But that just isn’t the right approach! The best models and algorithms out there work on high-end machines and require high data storage and manipulation resources. Constraints can be hardware or software as well.
    • Data storage capacity limits the amount of data that we can store for training and testing phases.
    • Hardware constraints allow us to choose algorithms which run according to the hardwares available to us. A self-learning ML enthusiast won’t be running a high-level algorithm which requires high computational power on his/her low-end machine.
    • Depending upon time constraint, whether we can allow training phase to be of long duration or not, or testing phase to be short or long is an important choice that we need to make before narrowing down our model.
  • Find available algorithms: Only after going through above three phases, we can check which algorithms adhere to our requirements, and constraints and finally go and implement it! 

To see how random forest works, we must know first how a decision tree works-

A decision tree is essentially a human making decision about a problem but without any prior knowledge about the problem itself. Just like that human, our decision tree learns from the experience given to it by us in form of datasets. Based on these experiences, it predicts the output. Each decision tree takes as input a set group of inputs and it is expected that for real problems, we provide it inputs that are in the similar form as of the training data. But when we expand our domain and try all random subsets of the features (inputs) via multiple trees and predict output based on the most common consensus, we arrive at Random forest algorithm.


Example

Suppose a group of students is tasked with finding how much will it rain on a particular day. Assume none of the students have any background knowledge or expertise in this area. Each of the students will, with help of their different (random) source of knowledge (subsets of features), try to give a prediction. One student may be using radio weather forecast, one might be using Accuweather while one might be taking help of a friend in the weather department. Our goal then simply becomes to take all these predictions and provide a final complete prediction based on either average of all outputs of students (continuous data) or voting on the classes (discrete data). Now replace students with decision trees and we get random forest! As the sources of decision trees become more diverse, the final prediction from random forest becomes more and more accurate.

The more the number of Machine Learning algorithms that you implement, the faster and the more efficient your subsequent solutions and implementations become. The process that you may follow for the implementation of Machine Learning algorithm include the following:

  1. Select a programming language: Choose the programming language that you wish to use in order to undertake your implementation. This decision of choosing the programming language will influence the standard libraries as well as the APIs that you are going to make use of in your implementation.

  2. Select the algorithm that you wish to implement: Once you have chosen your programming language, the next logical step is to choose the algorithm that you are aiming to implement from scratch. Decide on all of the specifics of the algorithm and be as decisive and precise as possible. This means that you should decide on everything from the type of algorithm, the classes as well as the specific implementation and the description of what you wish to implement.

  3. Select the problem you wish to work upon: Next, move on to the selection of the canonical problem set that you are going to use in order to test and  validate the efficiency and correctness of your algorithm implementation. 

  4. Research the algorithm that you wish to implement: Look for and go through books, research papers, libraries, websites and blogs that contain descriptions of your algorithm, its implementation, conceptual understanding etc. Taking multiple descriptions and outlooks on your algorithm is important as it helps you give a total overall perspective of the uses and implementation methodologies of your algorithm. It also helps you overcome roadblocks and any wrong assumptions that you may have made about your algorithm.

  5. Undertake unit testing: Develop and run unit tests for each and every function of your algorithm. You will also do good by considering the test driven development aspects of your algorithm from the very initial phases of development. This also enables and forces you to understand the expectations as well as the purpose of each unit of code of your algorithm.

Machine Learning Certification & Courses

Python is one of the most popular languages for the purpose of Machine Learning. At KnowledgeHut, we have curated some of the best resources and videos from all over the internet. Most resources that are included as a part of the Python Machine Learning course at KnowledgeHut are drawn from some of the top notch Python conferences such as PyCon as well as PyData etc, created by some of the world’s top Data Scientists.

Much of the resources that are offered to learners at KnowledgeHut are hands-on tutorials. These tutorials are all accompanied by codes that in order to help them implement the algorithm or the program taught in that particular tutorial. Real world data sets are also included along with the said tutorials offered by KnowledgeHut.

While the basic concepts and systems of Machine Learning are important parts of a Machine Learning project, the following are some of the most essential topics of Machine Learning that every learner of Machine Learning must be thoroughly acquainted with:

  • Decision Trees: A Decision tree is a type of a supervised learning algorithm that is used for classification problems. Decision trees aid the system in deciding on which features are to be chosen and which conditions are to be used in order for splitting. They also help the system in determining the conditions for the stopping of splitting and ending a particular iteration.

    Advantages of decision tree methods
    • They are relatively simplistic
    • They are easy to understand, visualize and interpret.
    • They are able to implicitly perform feature selection as well as variable screening.
    • Decision trees are not affected by non linear relationships between parameters.
    • Decision trees require minimal efforts in the direction of data preparation from the user.
    • Decision trees are able to handle and analyze both categorical as well as numerical data.
    • Decision trees are also able to handle problems that require multiple outputs.
  • Support Vector Machines: Support Vector Machines are a type of classification methodologies that provide a higher degree of accuracy in classification problems. Support Vector Machines can also be used in problems of regression as well. 

    Some of the benefits of a Support Vector Machine include the following:
    • Owing to the nature of convex optimizations, Support Vector Machines provide guaranteed optimality in the solutions that they provide. The solution is not a local minimum, but a global minimum, thereby guaranteeing it’s optimality.
    • They are useful in both, Linearly Separable (also known as Hard margin) as well as Non-linearly separable (also known as Soft Margin) data.
    • Feature Mapping, which used to be a huge burden on the computational complexity of an algorithm, is a reduced burden owing to the ‘Kernel Trick’ provided by Support Vector Machines, which are able to undertake the process of feature mapping by carrying out simple dot products.
  • Naive Bayes: The Naive Bayes algorithm is a classification technique that is based on the Bayes’ theorem. It works on the assumption of independence between the different predictors. In simpler terms, the Naive Bayes algorithm assumes that the presence of a particular feature in a said sample of data is independent of and unrelated to the presence of any other feature in that particular sample of data.Some of the advantages of the Naive Bayes algorithm include the following:
    • It is very simplistic technique of classification - all that the system is doing is performing a bunch of counts. 
    • It requires less training data as compared to other techniques used for classification.
    • It is a classification technique that is highly scalable.
    • It converges quicker than other traditional discriminative models.

  • Random Forest algorithm: The Random Forest algorithm is a supervised learning algorithm. It creates a forest of decision trees and randomizes the inputs so as to prevent the system from identifying any pattern in the input data owing to its order. The Random Forest is basically a collection of randomized decision trees that are trained with the bagging method.

    Some of the advantages of the Random Forest algorithm include the following:
    • A Random forest may be used for both, regression as well as classification problems and tasks.
    • It is easy to view the relative importance that a random forest assigns to the input features
    • It is a very easy to use and handy algorithm.
    • The number of hyper parameters included in a random forest are not high and are relatively easy to understand.
    • A Random Forest is able to produce a good prediction result.

Before starting a new job, it is important to know and understand the pay scale that that particular career choice offers to you, and how easy (or difficult) it is to obtain a higher salary. In the area of Machine Learning, there exist job opportunities to earn quite a generous salary. However, like most other jobs, the more experience you have, the more your salary. Generally, the range of salary in the field of Machine Learning can be pegged from 3.5 lakhs per annum for an amateur Machine Learning engineer who is just starting out, to 90 lakhs per annum for an experienced and expert Machine Learning engineer. 

The comprehensive Machine Learning Certification list was handpicked by our Data Scientist offered by Universities & Certification providers online.

We have compiled a list of best machine learning certificates and these courses are available for beginners, intermediate learners and experts. 

List of Best Machine Learning Certification online around the Globe:

  1. Machine Learning Certification - Stanford University
  2. Machine Learning Certification – University of Washington
  3. Data Science Specialization – John Hopkins University
  4. Machine Learning Training A-Z™: Hands-On Python & R in Data Science
  5. Machine Learning with TensorFlow on Google Cloud Platform
  6. Python for Data Science and Machine Learning Training 
  7. Data Science, Deep Learning, & Machine Learning Tutorial with Python
  8. Machine Learning by Georgia Tech
  9. Higher School of Economics - Advanced Machine Learning Certification
  10. Data Science and Machine Learning Tutorial with R
  11. Machine Learning at Udacity
  12. Advanced Machine Learning, Data Mining, and Artificial Intelligence
  13. Big Data Applications: Machine Learning at Scale
  14. AWS Machine Learning Certification
  15. Machine Learning Master’s Program 
  16. Introduction to Machine Learning (Masters)
  17. Introduction to Machine Learning (Ph.D.) by Carnegie Mellon University
  18. Topics in Deep Learning by Carnegie Mellon University
  19. Machine Learning by CMU- 10-315
  20. Advanced Introduction to Machine Learning by Carnegie Mellon University
  21. Machine Learning Fundamentals by the University California, San Diego
  22. MIT Open Course on Machine learning
  23. University of Michigan – Data Science with Python

For your reference, we've done in-depth research and selected the top courses based on a variety of factors, including rating, ranking, learning patterns and student reviews.

From the traditional methods of learning in the classroom to the new age ways of self-paced studies, the learning process has witnessed a huge variety and evolution. Today, we have a choice of studying through books, blogs, videos, events, and the list is endless. Depending on what type of learner you are, you can select one that is most suitable and according to your needs.

Confused? Well, this detailed certification list is going to help you choose what type of learner you’re. Based on the previous knowledge and grasping capacity, learners are classified into 3 categories-

Beginners- If you have no prior knowledge and are learning the subject for the first time, then you fall into this category.

Intermediate- The one who has some knowledge on the subject and past experiences. 

Advanced- This is also called an expert zone. With lots of practice, experience, and mistakes, one achieves enough knowledge of the subject to be recognized as an Expert.

One should always remember, “Every expert was once a beginner.”

Now, as each one of us is different, so is our learning style. The types of Machine Learning learners are broadly classified into 7 groups.

  1. Visual or spatial- These learners prefer using visuals like pictures, images, and spatial understanding.

  2. Aural or auditory/musical- Does music help you concentrate better? If it does, then you fall into this category. An Aural learner understands better using sound and music.

  3. Verbal or Linguistic- Do you remember better when you have practiced speaking it out loud? Well then, this is your arena. A verbal learner prefers using words, both in speech and writing.

  4. Physical or Kinetic- Those with a highly developed sense of touch fall into this category. They prefer using their body, hands and utilize their sense of touch. 

  5. Logical or Mathematical- Does history and other theoretical subjects bore you? Do you find reasoning and logic more interesting? A logical learner uses logic, reasoning, and system to enhance their understanding.

  6. Social or Interpersonal- Being a social animal, it’s understandable that many of us prefer being in companies of our friends. A social learner is the one who prefers studying in groups.

  7. Solitary or Intrapersonal- Away from the chaos, learning at your own pace. That’s what solitary learners prefer. They like to study alone and use self-study to improve their knowledge.

Now that we’re done with the types of learner, did you figure out which one you’re? A majority of us prefer the traditional classroom learning. Some of us prefer self-study. So, whatever your choice is. Below, we have a list of top Machine Learning certifications and Training that will help you understand and enhance your ability on the topic.

The best part is, it doesn’t matter if you’re half a world away from the institution as these courses are available online. Also, whether you are a beginner or an intermediate, these classes are designed in such a way that anyone can learn from them based on their own level of understanding.

As mentioned above, these courses have been chosen based on a variety of factors, including rating, ranking, learning patterns and student reviews.    

The best part is, it doesn’t matter if you’re half a world away from the institution as these courses are available online. Also, whether you are a beginner or an intermediate, these Machine Learning certifications are designed in such a way that anyone can learn from them based on their own level of understanding.

As mentioned above, these 23 Machine Learning courses have been chosen based on a variety of factors, including rating, ranking, learning patterns and student reviews.

23. University of Michigan – Data Science with Python 

  • About the course
    • With an impressive rating of 4.5/5, this curriculum by the University of Michigan is one of the best courses available. 
    • The Course is 100% Online with a flexible schedule.
    • The duration will be approximately 5 months if you learn 7 hrs/week.
    • The vocal language is English, but the subtitles are provided in multiple languages including Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, Hebrew.
  • Benefits of University of Michigan’s Data Science with Python course

    Here, you’ll learn to deal with various popular Python techniques such as Applied Plotting, Applied Machine Learning, Applied Text Mining, and Applied Social Network Analysis. Using these techniques, one can learn to
    • Inspect the connectivity of a social network
    • Manage an inferential statistical analysis
    • Observe whether a data visualization is good or bad
    • Improve a data analysis with applied machine learning
  • What Machine Learning skills (ML) will you learn
    • Text Mining
    • Python Programming
    • Pandas
    • Matplotlib
  • Level- Intermediate
  • Type- Certificate
  • Instructors- Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran
  • Cost of the Data Science Course- ₹ 3406 in India and $ 48.08 in the US

The course is available on Coursera


22. MIT Open Course on Machine learning

  • About the course
    • This course is provided by the Massachusetts Institute of Technology. 
    • This course has been archived but you can still access the course material. 
    • It gives an overview of many concepts, techniques, and algorithms in machine learning.
  • Benefits of the MIT Open Machine Learning course
    • This tutorial aims to cover the underlying machine learning algorithms.
    • Starting from Regression, Classification till higher level concepts such as Bayesian networks, collaborative filtering etc. 
    • It is available for download in PDF version
  • What Machine Learning skills you’ll learn
    • Classification and linear regression
    • Boosting, Support vector machines
    • Hidden Markov models, and Bayesian networks 
    • Modern machine learning methods 
    • Statistical inference 
  • Hands-on Projects
    • Apply/Develop a machine learning method to solve a specific problem
      • A machine learning approach to classifying your incoming mail
      • Predict stock prices based on past price variation
      • Predict how people would rate movies, books, etc.
      • Cluster gene expression data, how to modify existing methods to solve the problem better
    • Surveys/Reviews
      • Complexity of classifiers, different concepts, comparison
      • Algorithmic stability, which methods have stability guarantees, and where could we apply these concepts
      • Collaborative filtering, what methods are available to solve collaborative filtering problems, in which context have they been found effective
      • Machine learning methods for genomic data, are they effective, what is missing
      • Calibration, which methods are calibrated, how to modify a method so as to improve calibration

    • Theoretical problems
      • Generalization guarantees for a specific algorithm (ask us)
      • Learnability of specific concept classes (ask us)
      • Convergence/consistency of a specific estimation method (ask us)
  • Level - Graduate
  • Instructors- Rohit Singh, Prof. Tommi Jaakkola, Ali Mohammad are the trainers for the program.
  • Cost- ₹ 1,38,128 in India and $1,950 in US

The course is available here.

21.  Machine Learning Fundamentals by the University California, San Diego

  • About the course
    • Offered by the University of California, San Diego (UCSanDiegoX)
    • In this course, you will learn a variety of supervised and unsupervised learning algorithms. 
  • Benefits of UCSanDiegoX’s ML Fundamentals course
    • A free course which can be upgraded.
    • Understanding the role of Machine learning in the Real world.
    • Use of ML in data-driven modeling, prediction and decision making. 
    • The knowledge that you build from here will help you analyze many different types of data and to build descriptive and predictive models.
    • Learn to classify images, identify salient topics in a corpus of documents and partition people according to personality profiles using real case studies.
  • What Machine Learning skills you’ll learn
    • Classification, regression, and conditional probability estimation
    • Generative and discriminative models
    • Linear models and extensions to nonlinearity using kernel methods
    • Ensemble methods: boosting, bagging, random forests
    • Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
  • Level- Advanced
  • Type- Certificate (Paid)
  • Instructors- Sanjoy Dasgupta, Professor of Computer Science and Engineering, UC San Diego.
  • Cost- ₹ 24,792 in India and $350 in US

This course is available here.


20. Advanced Introduction to Machine Learning by Carnegie Mellon University

  • About the course 
    • Offered by Carnegie Mellon University
    • The course is more statistics-based and less systems-engineering focused.
  • Benefits of the CMU Advanced ML Classes classes course
    • In-depth knowledge of the course, including Deep Learning.
    • To prepare students to write research papers that rely on and contribute to machine learning.
    • Applying Mathematics and algorithms to do research and applications in machine learning.
    • Ph.D. students who are studying near subjects or want to utilize data science and machine learning to accomplish the need of the hour will also find this course extremely useful.
  • What Machine Learning skills you’ll learn
    • the algorithms, mathematics, theories,
    • data mining of historical health records
    • speech recognition systems
    • insights needed to do in-depth research and applications in machine learning. 
  • Level- Advanced
  • Type- Ph.D. Course
  • Instructors- To be taught by most experienced professors of CMU.
  • Cost- ₹ 17.5 L per semester in India and $ 24,500 per semester in US. 

This course is available here.


19. Machine Learning by CMU- 10-315

  • About the course
    • Offered by Carnegie Mellon University
    • This course will help understand about the core principles of machine learning.
  • Benefits of the CMU ML course–
    • Designed for undergraduate students who are interested in Programming.
    • From robotics to Medical sciences, this course teaches you to understand the different types of tasks based on many types of experience. 
    • These experiences are further nurtured and applied based on the need of the classes.
    • Learning how to select and apply appropriate machine learning algorithms for a specific problem
  • What Machine Learning skills you’ll learn
    • Concept learning
    • Decision trees
    • Hypothesis testing
    • Bayesian learning
    • MDL principle
    • Gibbs classifier
    • Naive Bayes
    • Bayes Nets Graphical Model
    • EM algorithm
  • Level- Beginner
  • Instructors- To be taught by most experienced professors of CMU.
  • Cost- ₹ 17.5L in India per semester and $. 24,500 in US per semester. 

This course is available here.


18. Topics in Deep Learning by Carnegie Mellon University

  • About the course
    • Offered by Carnegie Mellon University
    • One of the most in-depth courses available on the subject.
    • This course is offered for those who are not currently at Carnegie Mellon University.
  • Benefits of CMU Deep learning course
    • Build intelligent machines that are capable of extracting meaningful representations from high-dimensional data.
    • Solve many AI-related tasks.

What Machine Learning skills you’ll learn

    • Learn useful representations by re-using and combining intermediate concepts
    • Apply these models in a wide variety of domains, including visual object recognition, information retrieval, natural language processing and speech perception. 
  • Level- Advanced
  • Type- Master and PhD.
  • Instructors- Most experienced professors of CMU.
  • Cost- ₹ 17.5L per semester in India and $. 24,500 per semester in US.

This course is available here.


17. Introduction to Machine Learning (Ph.D.) by Carnegie Mellon University

  • About the course
    • Offered by Carnegie Mellon University
    • From robotics to Medical sciences, this course teaches you to understand the different types of tasks based on many types of experience. 
    • These experiences are further nurtured and applied based on the need of the classes.
  • Benefits of CMU’s Intro. to Machine Learning (Ph.D.) course
    • Learn to answer the universal question of How can we build computer programs that automatically improve their performance through experience.
    • The curriculum is designed to balance and lift Ph.D. students with all background even if they no advanced knowledge of ML.
  • What Machine Learning skills you’ll learn
    • Data mining of historical health records
    • Speech recognition systems
    • Apply Mathematics and algorithms to do research and applications in machine learning.
  • Level- Advanced
  • Type- Ph.D. Course
  • Instructors- To be taught by most experienced professors of CMU.
  • Cost- ₹ 17.5L per semester in India and $. 24,500 per semester in US. 

This course is available here.


16. Introduction to Machine Learning (Masters)

  • About the course 
    • Offered by Carnegie Mellon University
    • This course is offered for those who are not currently at Carnegie Mellon University.
  • Benefits of CMU’s Introduction to Machine Learning
  • Understanding the different types of tasks based on many types of experience that are gained through this Introduction to machine learning course CMU.
  • What Machine Learning skills you’ll learn
    • Concept learning
    • Decision trees
    • Neural networks
    • Linear learning
    • Active learning
    • Estimation the bias-variance tradeoff
    • Hypothesis testing
    • Bayesian learning
    • Boosting and discriminative training and a lot more.
  • Level- Beginner
  • Type- Master Course
  • Instructors- To be taught by most experienced professors of CMU.
  • Cost- ₹ 17.5L per semester in India and $. 24,500 per semester in U    

This course is available here.


15. Machine Learning Master’s Program 

  • About the course
    • Offered by Carnegie Mellon University
    • This course is offered for those who are not currently at Carnegie Mellon University.
  • Benefits of the CMU’s ML Master course
    • Understanding the role of Machine learning based on Practical Knowledge through the machine learning course CMU.
    • The curriculum is divided into 4 semesters with 1-semester offering internship which will allow the learners to apply what they have learned.
  • What Machine Learning skills you’ll learn
    • Data mining of historical health records
    • Speech recognition systems
    • Apply Mathematics and algorithms to do research and applications in machine learning.
  • Level- Beginner 
  • Type- Master Course
  • Instructors- To be taught by most experienced professors of CMU.
  • Cost- ₹ 17.5L per semester in India and $. 24,500 per semester in US. 

This course is available here.


14. AWS Machine Learning Certification

  • About the course
    • Offered by the Amazon Web Services (AWS)
    • The course originally developed by the Amazon Intel.
  • Benefits of the AWS ML course 
    • Hands-on Lab and documentation.
    • Usage of Amazon cloud makes it possible for the learners to get access to the resources and save the date anywhere, anytime.
    • Not just useful for students, but also for developers, data scientists, data platform engineers, and business decision makers.
    • Tailored Path makes it easier to get the hang of the concept.
    • A certification from Amazon.
  • What Machine Learning skills you’ll learn
    • For everyone who wants to use learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value.
    • Using their practical knowledge and years of business experience and combine it with Machine Learning to build something new.
  • Type- Master Course
  • Level- Advanced 
  • Cost- ₹ 24,792 in India and $350 in the US

This course is highly beneficial for those who are preparing for the Machine learning examinations. It has several mock exam trials and solutions.


The course is available here


13. Big Data Applications: Machine Learning at Scale

  • About the course 
    • This course is offered by Yandex.
    • Duration – 5 weeks with 6-8 hrs of Study/week.
  • Benefits of the Big Data Course 
    • Identify practical problems which can be solved with machine learning
    • Build, tune and apply linear models with Spark MLLib 
  • What Machine Learning skills you’ll learn
    • Text processing 
    • Fit decision trees and boost them with ensemble learning 
    • Construct your own recommender system.
  • Level- Advanced
  • Type- Certificate
  • Instructors- Vladimir Lesnichenko, Pavel Mezentsev, Emeli Dral, Alexey A. Dral, Ilya Trofimov, Evgeny Frolov
  • Cost- ₹ 3406 in India and $ 48 in the US

This course is available here. 

12. Advanced Machine Learning, Data Mining, and Artificial Intelligence 

  • About the course
    • This course is offered by Harvard Extension School.
    • The class is tutored by the Artificial Intelligence and Machine Learning Technical Lead, of Pfizer, Inc., Peter Vaughan Henstock
  • Benefits of the Harvard Course
    • The objective is to combine the theory with the hands-on practice of solving modern industry problems with an emphasis on image processing and natural language processing. 
    • Some industry standard tools will be introduced to prepare students for artificial intelligence jobs.
  • What Machine Learning skills you’ll learn
    • Outlier detection
    • Advanced clustering techniques
    • Deep learning
    • Dimensionality reduction methods
    • Frequent itemset mining, and recommender systems
  • Level- Advanced
  • Type- Master Course
  • Instructors- Peter Vaughan Henstock, PhD
  • Cost- ₹  1,94,865 in India and $2750 in the US

The course is available here


11. Machine Learning at Udacity

  • About the course
    • Rating – 3.9/5
    • Duration – 4 months with 5-10 hrs of study/week
    • Access to a hiring partner network 
  • Benefits of the Udacity ML course
    • Teaching students how to apply concepts of Machine Learning using interactive practice videos.
    • Recognized Nanodegree certification 
  • What Machine Learning skills you’ll learn
    • Supervised, unsupervised & reinforcement learning are covered using real-life examples.
  • Level- Intermediate
  • Type- Nanodegree Certification
  • Instructors- Jonathan Larkin, Cindy Lin, Arpan Chakraborty, Elizabeth Otto Hamel
  • Cost- ₹ 49900 in India and $704 in the US

The course is available here


10. Data Science and Machine Learning Tutorial with R

  • About the course
    • Rating of 4.6/5
    • The course is 100% Online with full lifetime access. 
    • Includes 17.5 hrs of Video lesson, 8 articles, and 3 downloadable materials.
    • Available in both Mobile and Computer.
    • This course is available on udemy
  • Benefits of the R Data science course
    • This course is designed for both beginners with no coding experience. The only requirement is a basic maths.
    • Full lifetime access of course
  • What skills you’ll learn
    • Program in R
    • Use R for Data Analysis
    • Create Data Visualizations
    • Use R to handle csv,excel,SQL files or web scraping
    • Use R to manipulate data easily
    • Use R for Machine Learning Algorithms
    • Use R for Data Science
  • Level- Beginner
  • Type- Certification
  • Instructors- Jose Portilla
  • Cost- ₹ 12,480 in India and $176 in the US

The course is available here.


9. Higher School of Economics - Advanced Machine Learning Certification

  • About the course
    • Rating – 4.8/5.
    • The Course is 100% Online with a flexible schedule.
  • Benefits of the Higher School of Economics Advanced ML course
    • The Course is divided into 7 layers. By the end of which you will be able to efficiently utilize methods in business and industry.
    • A team of 21 skilled professors.
  • What Machine Learning skills(ML) will you learn?
    • Deep learning
    • Reinforcement learning, natural language understanding, computer vision, and Bayesian methods. 
    • Real-life experiences of CERN scientists.
  • Level- Advanced
  • Type- Certificate
  • Instructors- Mikhail Hushchyn, Alexey Zobnin, Alexey Artemov, Sergey Yudin
  • Cost- ₹ 3406 in India and $48 in US

The course is available here.


8. Machine Learning by Georgia Tech

  • About the course
    • A graduate-level course covering the area of Artificial Intelligence concerned with computer programs
  • Benefits of the Georgia course 
    • One of the most detailed courses available.
    • This program allows you to master valuable data and AI skills
  • What Machine Learning skills you’ll learn
    • Supervised, unsupervised learning
    • Randomized optimization techniques (e.g. genetic algorithm)
    • Reinforcement learning, and even introductory game theory concepts
  • Level- Advanced
  • Type- Master Course

The course is available here


7. Data Science, Deep Learning, & Machine Learning Tutorial with Python

  • About the course
    • Rating of 4.5/5
    • Taught by Frank Kane, an experienced educator.
  • Benefits of this course
    • The course is 100% Online with full lifetime access. 
    • Requirements are only basic programming experience and high-school level mathematics.
  • What Machine Learning skills you’ll learn
    • Create artificial neural networks.
    • Make predictions by using algorithms of linear regression, polynomial regression, and multivariate regression.
    • Getting acquainted with reinforcement learning and how to build the world famous, Pac-Man bot.
    • Evaluate images, data, and sentiments using deep learning.
  • Type- Certificate
  • Level- Intermediate
  • Instructor- Frank Kane
  • Cost-₹ 10,240 in India and $143 in the US

The course is available here


6. Python for Data Science and Machine Learning Training 

  • About the course
    • Rating – 4.6/5
    • Includes 22.5 hrs of Video lesson, 10 articles, and 3 downloadable materials.
  • Benefits of the ML course by Udemy
    • The course is 100% Online with full lifetime access. 
    • Requirements are basic programming experience.
  • What Machine Learning skills you’ll learn
    • Implementing python techniques for Data learning and Machine learning.
    • How to use Sparks for big Data inspection.
    • Popular algorithms like Linear and Logistic Regression.
    • K-means Clustering 
    • Apply Matplotlib for Python Plotting. 
    • Also includes Random Forest and Decision trees.
  • Type- Certificate
  • Level- Intermediate
  • Instructors- Jose Portilla.
  • Cost- ₹ 12,480 in India and $175 in the US

The course is available here.

5. Machine Learning with TensorFlow on Google Cloud Platform

  • About the course
    • Rating – 4.5/5
    • The course is 100% Online with a flexible schedule. 
  • Benefits of ML course with TensorFlow on Google Cloud Platform 
    • One of the shortest courses with a duration of 1 month if proceeded with a pace of 15 hrs/week. 
    • Requirements are basic programming.
  • What Machine Learning skills will you learn?
    • Tensorflow
    • Machine Learning
    • Feature Engineering
    • Cloud Computing
  • Type- Certificate
  • Level- Intermediate
  • Cost - ₹ 3406 in India and $48 in US

The course is available here.


4. Machine Learning Training A-Z™: Hands-On Python & R in Data Science

  • About the course
    • The course is 100% Online with full lifetime access.
    • Offers a detailed 40 hrs of Video lesson, 27 articles, and 3 downloadable materials
  • Benefits of the A-Z course 
    • Kirill Eremenko and Hadelin de Ponteves worked alongside their team of data scientists to create this course for you. 
    • The vocal language is English, but the subtitles are provided in multiple languages including French, Portuguese (Brazilian), Japanese, Italian, Spanish and German.
    • The only requirement is basic high school mathematics. 
    • This course allows you to master Machine learning through not just one, but dual languages namely, Python and R.
  • What Machine Learning skill (ML) will you learn?
    • Master Machine Learning on Python & R
    • Make accurate predictions
    • Make powerful analysis
    • Make robust Machine Learning models
    • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
    • Handle advanced techniques like Dimensionality Reduction
    • Build an army of powerful Machine Learning models and know how to combine them to solve any problem
  • Type- Certificate
  • Level- Beginner 
  • Instructor- Kirill Eremenko, Hadelin de Ponteves
  • Cost- ₹ 960 in India and $13 in US

This course is available here.


3. Data Science Specialization – John Hopkins University

  • About the course
    • Rating – 4.5/5
    • The Course is 100% Online with a flexible schedule.
  • Benefits of the JHU course
    • This is the perfect course if you want to build your knowledge of Data Science.
    • The verbal language is English but subtitles are present in Arabic, French, Chinese (Simplified), Greek, Italian, Portuguese (Brazilian), Vietnamese, Russian, Turkish, Hebrew and Japanese.
    • The course is divided into 9 layers which allow the student to understand and comprehend the concepts one by one.
  • What Machine Learning skill (ML) will you learn?
    • From cleaning, visualizing to analyzing data using R.
    • Carrying out Regression analysis, interference and least squares through regression models.
    • Managing Data Science with the help of GitHub. 
    • After the completion, of course, the student will be assigned a project which will allow them to use all their skills developed during the training period.
  • Level- Intermediate
  • Instructors- Roger D. Peng, Brian Caffo and Jeff Leek
  • Cost- ₹ 3406 in India and $47 in US

The course is available here


2. Machine Learning Certification – University of Washington 

  • About the course
    • Rating- 4.6/5
    • The Course is 100% Online with a flexible schedule.
  • Benefits of the Washington University course
    • The verbal language is English, but the subtitles are provided in multiple languages including Chinese (Traditional), Portuguese (Brazilian), Vietnamese, Korean, Hebrew.
    • This course is designed to help you get a vast understanding and become accomplished at machine learning. 
    • This course is more about the practical aspect and that’s what makes it different from others.
  • What Machine Learning skills (ML) will you learn?
    • Real-life usage of different aspects of Machine Learning.
    • Prediction, classification, and cluster.
    • Inspecting large data using ML.
    • Build intelligent systems that can adapt and learn over time.
    • Create smart applications that can predict on its own.
  • Level- Advanced specialization
  • Instructor- Emily Fox and Carlos Guestrin
  • Cost- ₹ 3406 in India and $48 in US.

The course is available here.


1. Machine Learning Certification - Stanford University

  • About the course
    • This course is offered by the prestigious Stanford University
    • Rating of 4.9/5
    • Taught by Andrew Ng, CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain
  • Benefits of Machine Learning Certification - Stanford University
    • The Course is 100% Online with a flexible schedule.
    • The duration is approximately 2 months with an idle pace of 7 hrs/week.
    • The verbal language is English but subtitles are present in Arabic, French, Chinese (Simplified), Greek, Italian, Portuguese and Hindi.
  • What Machine Learning skills you’ll learn
    • Supervised and Unsupervised learning.
    • In-depth analysis of the best practices of Machine Learning.
    • Applying algorithms to build smart robots.
    • Developing bots capable of text understanding, medical informatics, and the new age, tech currency, data mining.
    • An understanding of Human-AI interaction.
    • Using Logistic Regression and learning Artificial Neural Network.
  • Level- Advanced
  • Instructor- Andrew Ng
  • Cost- ₹ 4101 in India and $58 in US

The course is available here.

That concludes our list of the top Machine Learning certifications. One thing is crystal clear, this is the era of Machine Learning and AI. Question is, how much do you know about it? And if you don’t, no worries. Remember, it’s never too late. Pick one of the above and dive into the world of infinite possibilities.

Of course not! You absolutely do not need a PhD in order to pursue this course. There does not arise a need for you to possess a PhD level understanding of the concepts and applications of Machine Learning, in order for you to take up this course. In the same way that a theoretical Computer Science programmer does not require a background of education in the field of Computer Science, you do not need a very deep and intimate understanding of the theoretical concepts of Machine Learning, in order for you to be able to gain a substantial amount of knowledge about the practical applications of the same.

At the end of this course on Machine Learning, students will be able to:

  • Possess a good understanding of the fundamental challenges and issues facing Machine Learning studies and the application of Machine Learning. Some of these challenges include quality of data, model complexity, model selection etc.

  • Appreciate the underlying mathematical relationships across and within Machine Learning algorithms as well as the paradigms of unsupervised as well as supervised learning. 

  • Will be able to design, implement and analyse the various Machine Learning algorithms in a range of real world applications, models and projects.

  • Possess an understanding of the strengths and weaknesses of several applications of Machine Learning projects.

  • Be able to understand, analyse and implement Machine Learning algorithms.

  • Be able to apply the theoretical Machine Learning concepts to solve real life problems.

Without a doubt, Andrew Ng’s course on Machine learning is still the best one out there. We have compiled some of the main reasons why this course is a must for any ML enthusiast.

  • The ease of understanding is high in this course making it easy to comprehend for both beginners as well as professionals.
  • Its curriculum is up-to-date with the current industry norms and is taught by Coursera’s co-founder Andrew Ng himself who also headed Google Brain and was a former chief scientist at Baidu.
  • At the end of the course, you are awarded a certificate based on your performance and final score. 
  • It makes sure that you understand the mathematics behind all these Machine Learning algorithms as well.
  • It has already been taken by thousands of students and professionals likewise due to its availability being online.
  • The course takes approximately 55 hours to complete and 7 hours/week which is quite acceptable for anyone.

Here’s taking a look at 6 of the most popular and useful Machine Learning courses that are available for free-

  1. Learn with Google AI
  2. Google - Machine Learning
  3. Stanford University- Machine Learning
  4. Columbia University- Machine Learning
  5. Nvidia- Fundamentals of Deep Learning for Computer Vision
  6. MIT-Deep Learning for Self Driving Carsl 
  1. Learn with Google AI: The Learn with Google AI is a recently launched learning resource that was launched as a part of Google’s efforts to broaden the understanding of Artificial Intelligence and Machine Learning among the public at large. New material is constantly being added to the course. However, it already contains a Machine Learning with TensorFlow course which can be accessed and studied for free. 

  2. Google - Machine Learning: This Machine Learning course is offered by Google in collaboration with Udacity. This course, however, is not aimed at complete novice developers and learners of Machine Learning. This Machine Learning course by Google focuses on Deep Learning as well as the design of self teaching systems that are able to learn, analyze and work on large and complicated data sets. This entire course can be studied on Udacity for free, although a learner would not be able to earn a certification for the same. A nominal payment would even get you a certification for the same as well.

  3. Stanford University- Machine Learning: This Machine Learning course offered by Stanford University is offered in collaboration with Coursera and is taught with Andrew Ng, who is the founder of Google Brain - the Deep learning unit of Google as well as the head of Artificial Intelligence for Baidu.

  4. Columbia University- Machine Learning: This Machine Learning course offered by Columbia University is also another popular Machine Learning course that is available for free on the internet. This course teachers the learners about the models and methods of Machine Learning, along with knowledge about the applications of these concepts to real world problems and applications.

  5. Nvidia- Fundamentals of Deep Learning for Computer Vision: This course offers by Nvidia helps the learner build the concepts of Deep Learning and Artificial Intelligence, starting with a basic understanding of Machine Learning concepts and fundamentals. Object recognition and image classification are two concepts that are stressed upon in this course.

  6. MIT-Deep Learning for Self Driving Cars: This course offered by MIT is taught using the approach of taking one real world application of Machine Learning and Artificial Intelligence and using it to explore the specific technology and concepts involved. The course covers the use of the MIT DeepTraffic Simulator. This Simulator challenges that learner to develop a simulated car and to build the same using the concepts of Machine Learning and Artificial Intelligence. 

Below are some of the top ML courses for those learners who have some experience in this field:

  • Machine Learning by Andrew Ng
  • Machine Learning A-Z™
  • Python for Data Science and Machine Learning Bootcamp
  • Understanding Machine Learning with Python

Machine Learning by Andrew Ng: Andrew Ng, co-founder of Coursera himself is the author of this course. This course is a must for all ML enthusiasts irrespective of their expertise level in the domain. Certified by Stanford University, it is a 55 hours course.
Machine Learning Bootcamp: Become an ML Engineer in 6 months. Job Guaranteed: This course is a must for job seekers in the ML field. It provides comprehensive knowledge and experience if you are serious towards the ML field as it is a 6 months course.
Artificial Intelligence by Columbia University: This course provides a solid understanding of AI concepts from a professional and graduate point of view. You will be implementing your ML skills to solve real-life problems and applications. This field also takes you to see the applications of AI in fields like robotics, vision and physical simulation. It is a 12 weeks course.

Mathematics for Machine Learning Specialization: Being an expert in ML directly relates to your expertise in mathematics. This course helps to understand the underlying mathematics in ML so that you can relate how it works in the Computer science field. It is an approx 2 months course.

Machine Learning with Python

Here's how you can get started on the use of Python for Machine Learning:
  1. Adjust your mindset and believe that you can apply Machine Learning Concepts.
  2. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
  3. Take a tour of the tool in order to get an idea of all the functionalities available and their uses.
  4. Load a dataset and make use of statistical summaries and data visualization in order to understand its structure and workings.
  5. Practice on some of the most commonly used and popular datasets so as to gain a better understanding of the concepts.
  6. Start small and work your way to bigger and more complicated projects.
  7. Gathering all this knowledge will eventually give you the confidence of slowly embarking on your journey of applying Python for Machine Learning Projects.

One of the fastest growing platforms for Machine Learning, Python is a world full of endless possibilities waiting to be explored. The following are 7 steps to follow in order to learn Python for Machine Learning effectively and easily:

  1. Begin with the Basics- It is difficult to understand anything in the absence of a knowledge of basic syntax. However, it is important to remember that you must not spend too much time on learning the syntax. Just begin by knowing enough about the syntax in order to be able to start with the implementation of your own basic project.

  2. Set up your system- Most resources recommend installing Anaconda on your system. Anaconda is an open source distribution for R and Python languages and is used for predictive analytics, large scale data processing and Scientific Computing. The Anaconda environment has every resource, tool and functionality that you need in order to begin your implementation of Machine Learning and Data Science.

  3. Learn Regular Expressions- Regular expressions are bound to come in handy since we have to deal with contextual data. Regular expressions are means of the detection and collection of corrupt errors from a set of records, tables or a database. Regular expressions are useful in the identification of incorrect, inaccurate, irrelevant and incomplete parts of the data collected. They also come in handy when such data is needed to be modified, replaced and/or deleted.

  4. Learn and install essential libraries for Machine Learning and Data Science- Libraries are bundles of pre-existing objects and functions that can be imported into the Python script that you are working on. Libraries are useful in saving a lot of time and effort by providing you with functionalities and code snippets automatically, without you having to actually work on them.

  5. Start implementing projects- The knowledge you gain by reading and minor project implementations is of no use unless you do not retain it well. And proper retention does not come without continuous practice and implementation of your acquired knowledge. Crafting and developing your own projects helps you push your capabilities, helps you learn more, pushes you to strive to acquire new skills as well as helps you build a portfolio of your very own projects.

  6. Start working on projects of your own- Once you are done working on projects that are simplistic and miniature in nature and have developed a reasonably good understanding of the basic concepts, it is best to move on to start working on some projects of your own. You may still need to look up references and concepts, but that is okay. Implementation is the key to learning.

  7. Keep working on more difficult projects- Keep working on more difficult and complex projects on your own. Once you are comfortable with the concept and complexity you are working on, it is then a signal for you to move on to projects with more complexity.

Some of the best free online books that facilitate learning the Python language include the following:

  1. Google Developers Python Course
  2. Python the Hard Way, authored by Zed A. Shaw
  3. An introduction to Python for Scientific Computing (from UCSB Engineering), authored by Scott Shell
  4. Automate the Boring stuff with Python
  5. Think Python
  1. Google Developers Python Course: This is highly recommended for all learners who are visually inclined and learn best through visual aids.

  2. Python the Hard Way, authored by Zed A. Shaw: This is a book that can be picked up and read by amateur Python learners who have absolutely no knowledge of Python or even programming in general.

  3. An introduction to Python for Scientific Computing (from UCSB Engineering), authored by Scott Shell: This book offers a brilliant insight into the introduction of Python, all in merely 60 pages.

  4. Automate the Boring stuff with Python: This is freely available to download on the internet. It takes the reader through the process of getting started with and immersing in the world of Python programming in a very slow, considerate manner. The book dives into a lot of detail into the important Python concepts and is, therefore, a great learning resource for beginners.

  5. Think Python: How to think like a Computer Scientist: The more fundamental programming concepts are focussed on in this book titled “Think Python: How to think like a Computer Scientist”. 

Thanks to the large and diverse open source community of Python and its libraries, there are some very useful libraries as below:

  • Scikit-learn: Primarily for data mining, data analysis and in data science as well.

  • Numpy: Useful due to its high performance when dealing with N-dimensional arrays.

  • Pandas: Useful for high-level data structures and incredibly useful for data extraction and preparation.

  • Matplotlib: In almost every ML problem, we need to plot a graph for better data representation, matplotlib helps in the plotting of graph in 2D.

  • TensorFlow: If you are using deep learning in your project then this library, created by Google, is the go-to library as it uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.

The common steps involved to complete a Machine learning Project with Python include the following:

  1. Proper definition and structuring of the problem
  2. Preparing the data
  3. Evaluation of algorithms available in order to solve the problem.
  4. Improvisation of the results obtained by application of the chosen algorithm.
  5. Presentation of the results obtained.

SciKit is short for SciPy Toolkit. SciKits are add-on packages for SciPy which are too specialized to be kept in SciPy itself. They are developed and hosted independently from the main SciPy package. One of the most famous SciKit is a scikit-learn package. Below are some of its highlights:

  • It contains efficient and easy-to-use tools for data analysis and data mining.
  • As it has a BSD license, you can use it in your own project as well as for commercial purposes too.
  • It is built upon existing Python libraries such as NumPy, SciPy, and matplotlib.

Datasets can be loaded into your machine learning project by making use of Python. The most common format of datasets that are loaded into your project is the .CSV format used for CSV files. There are a number of ways in which you can load a dataset into your Machine Learning project. Some of these include the following:

  • Load CSV file with Python Standard Library: The Python Standard Library is a Python library that provides the developer with a wide number of modules that are inherently built into it. It is, thus, able to provide standardized solutions as well as system functionality to most common problems in Machine Learning projects. The following code snippet typed into the Command prompt is an example of loading a CSV file with the Python Standard Library:
    • import csv
    • import numpy as np
    • import matplotlib.pyplot as plt
    • data_path = 'data/EntreDhuisEtMarne.csv'
    • with open(data_path, 'r') as f:
    • reader = csv.reader(f, delimiter=',')
  • # get header from first row
    • headers = next(reader)
  • # get all the rows as a list
    • data = list(reader)
  • # transform data into numpy array
    • data = np.array(data).astype(float)
    • print(headers)
    • print(data.shape)
    • print(data[:3])
  • # Plot the data
    • plt.plot(data[:, 1], data[:, 0])
    • plt.axis('equal')
    • plt.xlabel(headers[1])
    • plt.ylabel(headers[0])
    • plt.show()
    • plt.plot(data[:, 2])
    • plt.xlabel('Table Index')
    • plt.ylabel(headers[2])
    • plt.show()

A file object is obtained in the above code snippet by opening the file the open() command. Post the closing of the with() block, the with() statement makes sure that the file is closed. This file is then made use of for the csv.reader which is then iterated over all the rows, thereby returning a list for each row with the items in the form of a string. Finally, this string is then transformed into a NumPy array which is then used for further processing of the data.

Load CSV file with NumPy: In order to load a CSV file with the help of NumPy, the functions numpy.genfromtxt and numpy.loadtxt are made use of. The difference between these two is that the np.genfromtxt function is able to read the CSV files that contain the missing data and provides the developer with options such as the missing_values and the filling_values parameters, whereas the numpy.genfromtxt function makes use of the names argument in order to load the header.

The code snippet for the above two functions are as follows:

    • For np.genfromtxt
      • data = np.loadtxt(data_path, delimiter=',', skiprows=1)
    • For numpy.genfromtxt
      • data = np.genfromtxt(datas_path, delimiter=',', names=True)

Load CSV file with Pandas: Python is quite a powerful Python library that is used for the purpose of data analysis. The main object in the Pandas library is termed as a dataframe, which is essentially a numpy array that contains rows and columns as well as labels for these rows and arrows. The way to load a CSV file in Python is to simply type in the following code snippet into the command prompt:

  • import pandas as pd
  • df1 = pd.read_csv("C:/PythonHow/income_data.csv")
  • print(df1)



The following are some tips to help you learn basic Python skills:

  1. Consistency is Key: Code every day. Consistency is very important when you are learning a new programming language. Commit to it and code every day. While it may be hard to believe, it is a fact that muscle memory plays a big role in programming. It may seem like a daunting task at first- but do not give up. Start small with coding for 25 minutes each day and progressively increase your efforts from there on out.

  2. Write it out: As you move further in your journey as programmer, there will be moments when you wonder if you should have taken notes from the beginning. Let us tell you-you should! Several studies have proven over the past several years that writing down a particular thing with your own hands, is the key to long term retention of the concept. This tip is especially beneficial for those programmers who are learning Python with the aim of becoming full time developers. Another benefit of writing down stuff by hand is that it helps you plan out your code on paper before you move to actually implementing it on your computer, where visualisation of your code is an issue at the beginning of your coding journey.

  3. Go interactive!: The interactive Python shell is one of the best learning tools, irrespective of whether you are writing code for the first time, learning about Python data structures such as dictionaries, list, strings etc or debugging an application. In order to initialize the Python shell, simply open your terminal and type in Python or Python3 (as the case may be) into the command line and hit Enter.

  4. Assume the role of a Bug Bounty Hunter: It is inevitable that you will run into bugs. The best way to pick up basic Python programming skills is to sit down and solve the bugs on your own. Do not let the bugs frustrate you. Instead, take up the challenge as a means of learning Python in the best possible way and take pride in becoming a Bug Bounty Hunter.

  5. Surround yourself with other people who are learning: Coding may seem like a solitary activity, but it actually brings out the best results when it is done in a collaborative manner. It is important for you to surround yourself with other people who are learning Python as well as this not only gives you a boost and keeps you going, but also helps you receive helpful tips and tricks from other, along the way.

  6. Opt for Pair programming: Pair programming in a technique in which two developers work on a particular piece of work/ code together. One programmer acts as the Driver, while the other acts as a Navigator. The driver of the code is the one who is actually writing the code, while the Navigator is the developer who guides the entire process, gives reviews and feedback as well as confirms the correctness of the code while it is being written. Pair Programming not only helps a developer learn mutually from other developers but also exposes him/her to multiple ways of thinking and ideas and gives them a fresh perspective on debugging, problem solving or even on writing the code itself.

Due to its vast open-source community, there are tons of libraries for you to play with, but depending upon their ease in implementation, performance, the open source community and so on, we have compiled a list of Python libraries which are best for machine learning.

  • Scikit-learn: Used majorly for data mining, data analysis and in data science as well.
  • SciPy: Contains packages for Mathematics, engineering, and science (manipulation).

  • Numpy: Provides much more than just efficiency - free and fast vector and matrix operations.

  • Keras: When one thinks Neural network, one goes to get the help of Keras.

  • TensorFlow: It uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.

  • Pandas: Provides high-level data structures. Significantly helpful while during data extraction and preparation.

  • Matplotlib: It helps in the visualization of data by plotting of graph in 2D.

  • Pytorch: If NLP is our aim, Pytorch is our go-to library. 

No, it does not take 2 to 3 hours for setting up Python and its libraries on the laptop. Depending upon the space available on your disk as well as the speed and version of your operating system, the installation of Python and its libraries on your laptop should not take more than 15 minutes.

Below are some major topics that can help you master Machine Learning with Python:

  • Programming skills: You will be coding your algorithm, and use code to prepare your data which is to be injected into the model you coded. It becomes, therefore, essential that you are comfortable with your programming skills.

  • Explore ML: It is recommended that you try to gather as much knowledge from different sources. Try to take online courses or classroom ones that can increase the breadth and width of your knowledge.

  • Data: Data is the most important part of your ML project because it is data upon which we apply our ML magic after all. Learn about different kinds of data, how to clean data, prepare data and work with data to gain insights. Learn about features (engineering) as well.

  • Libraries: This is not the most important part but an important one at least. There are tons of open source and free libraries out there for ML, but it is imperative that we choose and get ourselves acquainted with the libraries which are suitable, reliable, and easy to implement. For eg: pandas, SciPy etc.

  • Deep learning: After you have mastered basic ML stuff, it is time to move up to the advanced part of AI which is Deep learning. Deep learning is, in a way, adding of the brain to your algorithms as it replaces the need of a human expert or engineer to supervise it.

  • Polishing and Implementation: You cannot aim to master ML without implementing it in real life. Keep polishing on your mathematical and statistical skills as these are the ones that will be frequently used throughout your ML journey. Finally try some hands-on assignments from online courses which are available for free and get them checked with an expert to improve on your skills. The most effective way is to participate in competitions like Kaggle etc. 

What is Machine Learning/Intro to ML

The concept of Machine Learning deals with computers and systems taking in a huge amount of data, analyzing it and solving problems through training on that data in order to obtain the best possible outcome for a task or problem. It is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as why a particular approach to a problem actually works.

  • It's easy and it works

    Machines are able to work faster than human brains do and as such, are able to solve problems faster than we ever can. If, for example, there exist a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.

  • Being used in a wide range of applications today

    Machine Learning has several practical applications in real life. It is the very solution that the world was looking for a variety of problems. Machine learning drives businesses in that it helps them save time and money as well as effort. It is allowing people to get more things done in a more efficient, effective and appropriate manner. Every industry, starting from health care, nursing, transport, customer service to government and financial institutions are benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.

As both machine learning, as well as deep learning are part of the Artificial intelligence domain, their main application is the replacement of decision-making by humans. Some of the popular applications of machine learning and deep learning are:

  1. Medical diagnosis: Cancer cell detection, gene printing etc.

  2. Information retrieval: Search engines including both text as well as image search.

  3. Natural Language processing: Recommendation systems, photo tagging, and sentiment analysis.

  4. Finance: Stock predictions, trading, financial banking decisions.

  5. Computer vision: Real-time facial recognition, augmented reality, self-driving cars.

  6. Customer experience: Chatbots.

  7. Languages: Translations of text as well as images, language recognition of different dialects.
  • The first instance of a neural network was observed in the year 1943, when a Mathematician named Walter Pitts and a Neurophysiologist named Warren McCulloch wrote a paper about neurons and the way they work. The two researchers then moved on to creating a model connecting and resembling neurons by making use of an electric circuit.

  • Then, in the year 1950, famous scientist Alan Turing created the famous Turing Test. This test involves a computer that makes use of neural networks in order to deceive a human that it is not a computer, but a human.

  • In the year 1952, a man named Arthur Samuel created the first computer program which learnt along as and when it ran.

  • Another interesting incidence of the emergence of a neural network occurred in the year 1959, when two scientists named Marcian Hoff and Bernard Widrow created two models of neural networks at Stanford University.

    • The first of these models was called ADELINE. This model was able to detect binary patterns from among the input data.

    • The second model, which was the next generation of ADELINE, was named MADELINE. This model was able to detect and eliminate echos from phone lines, which is an application that is in use even today, 60 years after it was conceptualised.

    • Despite the success of the MADELINE model, no real progress was observed in the field of neural networks until the late 1970s. This was due to a combination of reasons, the most prominent of them being the popularity of the Von Neumann architecture. The Von Neumann architecture is a model wherein the data, as well as the instructions, are stored in the same memory, thus making it a simpler and arguably more easy to understand as compared to a neural network. Thus, people preferred to work on this model than on a neural network.

    • Popular interest in the field of Machine Learning and neural networks peaked in the year 1982, when a scientist named John Hopfield suggested the creation of a network with bidirectional lines, which was intimately similar to the workings of a neural network.

    • At the beginning of the 21st century, several businesses and large corporations realised that Machine Learning is inevitable as the future of businesses. This is when interest in the field really peaked as businesses increasingly exploited the potential of machine learning so as to boost efficiency, reduce errors and boost productivity, in general.

To complete a Machine learning Project with Python:

  1. Proper definition and structuring of the problem
  2. Preparing the data
  3. Evaluation of algorithms available in order to solve the problem.
  4. Improvisation of the results obtained by application of the chosen algorithm.
  5. Presentation of the results obtained.

Machine learning workflow is the order of steps and activities that are undertaken during the implementation of a Machine Learning project. Machine Learning takes place in 7 different phases, as a standard practice. These seven phases of a Machine Learning workflow include the following:

  1. Gathering data: Data gathering is a very important step in the process of Machine Learning as the quality as well as the quantity of the data gathered directly determines the correctness and the accuracy of your Machine Learning model. The data that you gather in this step is called the Training Data.

  2. Data Preparation: The training data gathered in the previous step is now to be placed at a suitable location as well as prepared for its proper use in Machine Learning training.
    • The data gathered is first put together, before randomizing its order. The randomization of this data is performed so that the order of the data does not affect the learning that is derived from this collected data. 
    • This step is also where we must perform any required pertinent visualization of the data so as to aid in the observation of any relevant relationships between the different variables, as well as to throw up any imbalances in the data.
    • In this step, we also split the data into two parts. The first part of the data will comprise the majority of the dataset. This is the part that will be used for the training of our Machine Learning Model. The second part will be made use of post the training, in order to evaluate the performance and accuracy of our Machine Learning model.
    • Other forms of adjustments such as normalization, de-duping, error correction etc are all performed here in the data preparation step. 
  3. Choosing the right model: The next step in a Machine Learning Workflow is to choose a Machine Learning Model. There exist many models that have been created by data scientists and researchers over the years. Some models are well suited for sequences such as music or text, while others are perfect for imaging data, numerical data, text based data etc. Thus, it is important for you to choose the right Machine Learning model depending upon the type of data you are working on and wish to classify.

  4. Training: The training step is also known as the bulk of Machine Learning. In the training step, we make use of the data that we have collected and worked on in the previous steps in an incremental manner so as to improve the ability of our model to predict a given pattern. In some ways, this step is like someone learning to drive for the first time. Initially, the individual does not know where the Accelerator, Clutch and the Gear are, they have no idea what the different switches and knobs do and are clueless about driving in general. 

    However, in time, with lots of practice and correction of mistakes, they slowly learn their way behind the wheel and maybe after a month, a year or two of driving, they become quite adept at it. In the same way, our Machine Learning model or algorithm has no clue about the data that is being fed to it in the beginning. However, a number of iterations later, it slowly becomes more adept at learning and analyzing the data being fed into it and over time, becomes quite adept at using, predicting and making decisions with the given data.

  5. Evaluation: Once the training stage is complete, it is now time to see if the model that you trained is any good at what it was expected to do in the first place. This is done by using the method of Evaluation.

    This is the step where we use the other portion of the dataset that we set aside initially. The evaluation phase enables us to test our model against the data that has not been used for training, thereby allowing us to observe how our model performs against new, unseen data. A rule of thumb such as an 80/20 or a 70/30 rule is best adapted so as to set standards of measurement for the data model. 

  6. Hyper Parameter Tuning: Once the evaluation step is complete, it is now possible for you to wish to develop, train and improve your work further. This can be done by undertaking tuning of the parameters. There were certain implicit decisions and assumptions that were made during the training phase- now is a great time to go back to those assumptions and test and try other values, so as to get more accurate results.

  7. Prediction: The goal of Machine Learning is to use data in order to answer questions. Thus, the prediction step is where our efforts bear fruit. It is the step where we get the answers to the questions that made us develop the model in the first place. 

Python Installation & Library

To install Python SciPy, we start by installing the latest available and stable version of Python 3, which is Python 3.6, at the time that this was written. 
In order to install Python, we will make use of the Anaconda Python package, which is to be installed in your system in the following ways:

  • Visit the Anaconda home page by clicking on the following link:

https://www.anaconda.com/

  • Click on the Anaconda tab from the menu and then proceed to click the Download tab in order to proceed to the Download page.
  • Once you land on the download page, choose the download that is suitable for your operating system viz. Linux, OSX or Windows.
    • Choose the Python 3.6 option
    • Proceed to choosing the Graphical Installer
  • The above steps will lead to the initiation of the installation of the Anaconda Package on your workstation.
  • Install Anaconda
    • Now that the Anaconda Package is downloaded on your system, we will then proceed to install Anaconda.
    • Before you proceed, please make sure that you have sufficient administrative privileges required in order to install software on your system.
    • Proceed to clicking on the downloaded Anaconda file.
    • This will open up the Anaconda installation wizard, which will allow you a quick and painless installation of the Anaconda package.
    • The installation of the Anaconda package will not take more than 1 GB of disk space on your hard drive.
    • The total process should not take you more than 10 minutes.
  • Initialize and update Anaconda
    • Once the Anaconda Python package is installed in your system, we will then proceed to check whether the Anaconda Python environment installed in your system is up to date.
    • The Anaconda Python package includes a full suite of Graphical Tools,  which are termed Anaconda Navigator. This Navigator can be launched from the application launcher.
    • From the Application Launcher window, choose the Conda option. Conda is chosen for this purpose as it simple and fast.
    • Open a command line window and confirm that conda is installed in your environment correctly by typing out the following command:
      • Conda -V
    • Next, confirm if python is correctly installed by typing out the following command in the command line:
      • Python -V
    • If the system does not throw up an error, it means that Conda was installed successfully.
    • Confirm that the Conda version is up to data by typing out the following command in the command line:
      • Conda update conda
      • Conda update anaconda
    • Next, confirm the SciPy environment in your system by copying the
      following script in a text editor:
      • import scipy
      • print('scipy: %s' % scipy.__version__)
      • # numpy
      • import numpy
      • print('numpy: %s' % numpy.__version__)
      • # matplotlib
      • import matplotlib
      • print('matplotlib: %s' % matplotlib.__version__)
      • # pandas
      • import pandas
      • print('pandas: %s' % pandas.__version__)
      • # statsmodels
      • import statsmodels
      • print('statsmodels: %s' % statsmodels.__version__)
      • # scikit-learn
      • import sklearn
      • print('sklearn: %s' % sklearn.__version__)
    • Save the above script in a file with the name versions.py
    • Switch back the editor for the command prompt and change your directory to the one where you saved the above file. When you run this file, i.e. by typing out the command “python versions.py”, you should be able to see an output that resembles the following:
      • scipy: 0.18.1
      • numpy: 1.11.1
      • matplotlib: 1.5.3
      • pandas: 0.18.1
      • statsmodels: 0.6.1
      • sklearn: 0.17.1

Installing Python involves following various steps described as under:

  • Click on this link in order to download the latest version of Python:
    https://www.python.org/downloads/

  • At the time of writing this, the latest release of Python is the Python 3.6.7. 

  • Double click on the executable file which is downloaded from the said link in order to launch the Python installer on your Windows Operating System. The following window will appear on your screen:

  • Choose the Customize Installation option and then press proceed.

  • Next, the following window will appear, which will show all the optional features available. All the features available are to be installed. These are checked by default on this screen.

  • Simply click on Next in order to proceed with the Python installation on your Windows Operating System.


  • On the next screen, the following window opens up, showing a list of advanced installation options. On this window, check all the options thatyou wish to install and then click on Next. Make sure you check the Install for all users option available on this screen.


  • You are now ready to install Python-3.6.7.
  • Once Python is installed on your system, try running Python on the command prompt. 
  • Type python or python3 into the command prompt. This command will throw up an error since we have not set the path yet.
  • In order to set the path for Python in your system, go to “My Computer”, and head over to Properties-> Advanced -> Environment Variables.
  • Next, Add the new path variable in the user variable section.



  • Type PATH as the variable name and then set the path to the installation directory of Python as shown in the following picture:

    • The path for Python is now set on your Windows system and is ready to be run locally. 
    • Restart the Command Prompt and type in python or python3 into the command prompt, as the case may be.
    • This command will now initialize and open the python interpreter shell, wherein you can now type and execute python commands and scripts.
  • Unlike MacOS, Python doesn’t comes preinstalled in windows but it doesn’t means that you cannot code in python on windows. You can easily install python on your windows by following any one of the below methods. Please also note that there are 2 most popular versions of python right now and you have to select which one to install (installing both works just fine as well):

    1. Python 2 - 2.7.13 
    2. Python 3 - 3.6.1

    Also keep in mind that projects made in Python 3 do not guarantee that they will run on the machines with python 2 installed.


    • Step 2: Go through the setup and install it via the GUI you downloaded.


    • Step 3:  After it is installed, you can check yourself by running the following command:
      python --version

      It must show the version of the python that you have recently installed.

    Python comes pre-installed on Mac OS X. You can check the same using below command:

    python --version

    If it displays your version number then python is installed and you can start coding in Python language. However, if it does not displays the version number then you can install it yourself by using any of the below 3 mentioned ways:

    Website: You can directly download .pkg from the Python’s official website.

    Continue through this setup process and python will be installed.


    • Homebrew: Brew is the most used and famous package manager for Mac. You can use brew to install multiple packages on your mac. Following command can be simply pasted onto your terminal after brew has been installed on the machine: brew install python.

    • Anaconda: One of the most reliable and easy way to install python and all its dependencies/libraries is via Anaconda graphical installer.

    It is always a good idea to check if your Python environment has been installed successfully and is functioning properly before you begin working on any Python, Machine Learning or Data Science project. The following script will help you make sure of the same and test your environment. 

    • Start and open a Command Line.
    • Initialise the Python interpreter by typing python into the command line.
    • Input the following few commands:
      1. # Python version
      2. import sys
      3. print('Python: {}'.format(sys.version))
      4. # scipy
      5. import scipy
      6. print('scipy: {}'.format(scipy.__version__))
      7. # numpy
      8. import numpy
      9. print('numpy: {}'.format(numpy.__version__))
      10. # matplotlib
      11. import matplotlib
      12. print('matplotlib: {}'.format(matplotlib.__version__))
      13. # pandas
      14. import pandas
      15. print('pandas: {}'.format(pandas.__version__))
      16. # scikit-learn
      17. import sklearn
      18. print('sklearn: {}'.format(sklearn.__version__))
    • After typing out and running the above script, you should get an output that resembles the following, 
      1. Python: 3.6.8 (default, Dec 30 2018, 13:01:55) 
      2. [GCC 4.2.1 Compatible Apple LLVM 9.1.0 (clang-902.0.39.2)]
      3. scipy: 1.1.0
      4. numpy: 1.15.4
      5. matplotlib: 3.0.2
      6. pandas: 0.23.4
      7. sklearn: 0.20.2

    • The above output must then be compared to the versions installed in your system.
    • Your versions installed and the output obtained must match, ideally. The APIs are not changed very often, so do not worry if you are left a few versions behind.
    • In case you get an error, go back and redo the process, right from installation to checking, until you get the correct result.

    Python is the most used language after MATLAB and R when it comes to Machine learning. Here below we mention the basic steps that you need to follow in order to get started with coding in ML. Basically, you require 2 main software packages which give you access to tons of libraries/packages that you can, in turn, use to create your ML project.

    Step 1: Install Python. Go to Python's official download website and install the desired version of python on your machine.

    Step 2: Download Anaconda. One of the most reliable and easy ways to install python and all its dependencies/libraries is via Anaconda graphical installer. While installing libraries via Conda, you can leave the work of decided the correct version of the package to be installed so that it is compatible with your python version to Anaconda itself. For eg: You want to use Theano in your next ML project, for that you will just type: conda install theano

    Step 3: Checking if you have these 2 installed or not by typing  the following commands:

    python -v

    You should be displayed the version of the python installed on your machine.

    conda -v

    If conda has been installed correctly, then this command must display the current version of conda

    Step 4: Installing libraries. After the setup of these 2 base software, you can start installing some of the most important and frequently used packages on your machine, such as scikit-learn, SciPy, NumPy, Keras etc.

    Note: You can always update all these libraries and even conda itself with the help conda command.

    conda update <library name>

    Python has a large and diverse open source community including scientists, researchers, mathematicians, programmers etc., making it best-suited tool for learning and even researching in Machine learning field. Some of the most popular Machine learning tools that Python provides are:

    • Scikit-learn: If you want to use ML, scikit-learn is the most important tool in the market.
      • In its algorithmic cheat sheet, it contains a prepared algorithm for classification, clustering, regressions, and dimensionality reduction issues.
      • Used majorly for data mining, data analysis and in data science as well.
    • SciPy: Contains packages for Mathematics, engineering, and science.
      • Helps in mathematical manipulation via NumPy, graphical via  Matplotlib, data structures, and their analysis via pandas among other significant packages.
    • Keras: When one thinks Neural network, one goes to get the help of Keras.
      • Includes support for both recurrent as well as convolutional neural network,
      • Runs smoothly with and without GPU and on CPU of course.
      • High-level API which can run on top of Google’s TensorFlow and Theano as well.
    • Pattern: Significant when dealing with Natural Language processing or web mining.
      • It is a data mining tools for Wikipedia, Twitter, and Google API.
      • Tools for NLP as well as network analysis.
    • Theano: If you are working with Multi-dimensional arrays, theano is the to-go tool.
      • Uses GPU for faster calculations than CPU.
      • It is tightly integrated with NumPy to provide faster number manipulation.

    Before diving into the how-to, we need to know why we need anaconda in the first place for machine learning, below are some of the reasons:

    • Whatever language you are programming on, implementing ML algorithms require not only one package but a collection of packages. These packages are managed and deployed by Anaconda.

    • With over 6 million users, and as it is an open source and free distribution, it is a stable package management software and contains more than 1400 data-science suitable packages for the popular OS like Mac OS, Linux and windows.

    Now as we know why we should be using Anaconda, below are the steps that define how to use anaconda effectively

    1. Download and Install software: First of all, the programming language should be installed and working. After that download and install anaconda software from here. Check by using the following command if conda is installed: conda -v

    2. Mathematical and scientific libraries: Confirm you have installed all the scientific libraries. For eg: SciPy, NumPy (python); klaR  (R)

    3. Install deep learning libraries: Install theano, Keras (python) or directly TensorFlow (R)  and start implementing them to create models.

    Note: You can also use Anaconda Navigator, GUI tool, for managing packages and launch applications rather than using the tradition command prompt.

    Machine Learning Books & Videos

    Whether you're a beginner or advanced, below is the list of free eBooks to help you learn Machine Learning include- 

    1. Machine Learning Yearning, by Andrew Ng
    2. Programming Collective Intelligence
    3. Machine Learning for Hackers, by Drew Conway and John Myles White
    4. Machine Learning by Tom M Mitchell
    1. Machine Learning Yearning, by Andrew Ng
      This book, penned by Andrew Ng is incomplete as yet and is a work in progress. However, several chapters of the book have been released and can be downloaded for free over the internet in the form of an ebook. Machine Learning Yearning is a book that will aid the reader in getting up to speed with the concepts of building Artificial Intelligence Systems. The book also teaches the reader to tune his/her mind to the various decisions that are required to be taken in the course of building a Machine Learning project.
    2. Programming Collective Intelligence 
      This is one of the best books to start with while embarking upon your journey of Machine Learning. There is not one data scientist who is involved in the field, without having read this book first. The book makes use of Python in order to deliver the concepts of Machine Learning, thereby making it a double whammy in your learning journey.
    3. Machine Learning for Hackers, by Drew Conway and John Myles White
      This is a book that is best recommended for learners who wish to develop an understanding of the basic functioning as well as the importance of Machine Learning Algorithms.
    4. Machine Learning by Tom M Mitchell
      This book, penned by Tom M Mitchell is a great book to start your Machine Learning journey with. It gives you a nice overview of the world of Machine Learning as well as the various vital Machine Learning algorithms and theorems. One speciality of this book is that it also provides you with the pseudocode summaries of the algorithms that it wishes to teach the learner. The book also contains several case studies as well as well planned examples that help the reader develop a basic understanding of the concepts of Machine Learning easily. 

    In case you are looking to read, go through and understand the basic concepts of Machine Learning before diving headfirst into the world of coding and learning Machine Learning through its applications, the following are two of the best free reference textbooks available on the topic of Machine Learning:

    1. An introduction to statistical learning with applications in R
    2. Elements of statistical learning
    1. An introduction to statistical learning with applications in R
      This book contains a gentler, slow-paced introduction of the elements of Statistical Learning that is aimed at learners of Machine Learning who possess little to no background of Machine Learning or the concepts at work behind the basic fundamentals of Machine Learning.

    2. Elements of statistical learning
      Data Mining, Inference and Prediction:  This book contains a more rigorous treatment of Machine Learning theory as well as the mathematics behind this treatment of Machine Learning.
    Here, we will be talking about the top 10 books in the field, followed by the top videos available and concluded by top webinars on the subject.

    Sr. No

    Book Title

    Author

    Originally Published
    Year

    Cost

    Pages

    Publisher

    Good reads Rating

    1

    The Hundred Page – Machine Learning Book

    Andriy Burkov

    January 2019

    Kindle Edition - ₹3031

    100

    eBook

    4.6/5

    2

    Hands-On Machine Learning with Scikit-Learn and Tensor Flow

    AurÈlien GÈron

    2017

    Paperback - ₹ 1275

    568

    O’Reilly Media

    4.5/5

    3

    Neural Networks and Deep Learning

    Michael Nielsen

    November, 2013

    Free

    NA

    Free eBook

    4.5/5

    4

    The Elements of Statistical Learning

    Trevor Hastie, Robert Tibshirani, and Jerome Friedman

    2001

    Kindle Edition - ₹2522

    745

    Springer-Verlag New York Inc

    4.4/5

    5

    Python Data Science Handbook

    Jake Vander Plas

    December 2016

    Paperback - ₹1250

    541

    O’Reilly Media

    4.3/5

    6

    Understanding Machine Learning from Theory to Algorithms

    Shai Shalev-Schwartz and Shai Ben-David

    2014

    Paperback - ₹ 904

    410

    Cambridge University Press

    4.1/5

    7

    Machine Learning

    Tom M. Mitchell

    1997

    Paperback - ₹ 418

    432

    McGraw Hill Education

    4.1/5

    8

    Programming Collective Intelligence

    Toby Segaran

    2002


    362

    O’Reilly Media

    4/5

    9

    Deep Learning

    Ian Goodfellow,
    Yoshua Bengio, and Aaron Courville

    2015

    Kindle Edition - ₹443

    800

    MIT Press

    4/5

    10

    Think Bayes

    Allen B. Downey

    September 2012

    Kindle Edition - ₹332

    214

    O’Reilly Media

    3.9/5

    1. The Hundred Page – Machine Learning Book
      • Intro - The most recent book on the list. Published this year and hit the #1 Bestseller spot in no time.  
        Andriy Burkov’s expert understanding of the complex subject and turning it into simple and comprehensible words are surely the reason this book dese
        The use of dual languages such as Python and R make it even more valuable.  
        Another very unique feature of the book is, ‘First Read, then Pay’.
      • Edition - As this book was published recently, this is the 1st Edition.

      • Format - It is available in 2 formats, as an eBook (Kindle Edition) and as a Hardcover.

      • Cost - Being at the top of the list, this book has high value. And therefore, it is expensive compared to others. The Kindle Edition costs ₹3031 whereas the hardcover comes at ₹4663.

      • Review - What is something that we all do before buying a book? Look at its reviews, right? Well, here is a review from one of the experts in the field –

      • “The breadth of topics the book covers are amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field." - AurÈlien Gèron,

      • Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlowTensorFlo

      • Rating - In Amazon ratings, the book has a combined rating of perfect 5/5 and the Goodreads rating is 4.6/5 (48 ratings).

      • Objective - The Objective of the book is to give as many details possible to the reader in just a hundred pages. Given the complexity of the topic, Burkov has definitely done a marvelous job as readers are actually able to grasp the concept.

      • Level - Beginner 
    2. Hands-On Machine Learning with Scikit-Learn and Tensor Flow
      • Intro - AurÈlien GÈron’s name might be complicated, but his take on the Machine Learning with real-life examples and graphics, sure made the subject understandable. 

      • Objective - The objective of the book is to develop the interest of the reader through real-life problems. Apart from the examples, this book features several training models on topics like vector machines, decision trees, random forests and ensemble methods to practice what one learns. The use of a Tensor flow library is a great way to build and train neural nets

      • Format - The book is available on Amazon in 2 formats – Paperback and Kindle Edition

      • Cost - The cost of the 2 formats is almost similar. Paperback costs ₹1275 and the Kindle Edition comes at ₹1401.

      • Rating - In terms of rating, this book has a rating of 4.5/5 from Amazon as well as Good Reads. (From 94 ratings).

      • Edition - This is the 1st Edition of the book as it was published in 2017.

      • Review - An Amazon customer review of the book,
        “This is one of the best books you can get for someone who is just starting out in ML, in its libraries such as Tensorflow, It covers the basics very good. As a book, it is 5/5 Once you are done with this book, the ideal next step is the "Deep Learning Book By Ian Goodfellow".

      • Level - Intermediate
    3. Neural Networks and Deep Learning
      • Intro - It’s a free eBook. Provided with the high-quality content, this makes it stand out from the rest since Machine Learning books are usually expensive. So, if you are looking for a pocket-friendly start, this is your path.

      • Objective - The objective of the book is to lucid take on the biologically-inspired programming model i.e., Neural Network which allows the Computer to learn on its own from the observed data, makes this eBook a perfect choice for beginners. 

        Michael Nielsen’s offers a powerful set of techniques for learning in neural networks in order to simplify Deep Learning.

      • Rating - It has received a rating of 4.47/5 from 203 ratings in the Good Reads. 

        “Thanks to this book, I can finally build my own neural net from scratch, not just run one line of code on tensorflow, caret or keras to get what I need. This is really a great tutorial: all codes provided, step-by-step improvement of an ANN to predict the MNIST dataset, interactive plots to understand the basics of neural nets, etc. It is easy to read, gives some good insights into the historical problems encountered in this field of research and how those problems got resolved. The concepts are very well illustrated. Simply perfect.” - A.N. Mignan

      • The overview given by Good reads -
        “Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.”

      • Format - It is only available in an eBook format. And is free of cost.

      • Level – Beginners and Intermediate
    4. The Elements of Statistical Learning
      • Intro - This is one of the oldest books on the list, which was published almost 2 decades ago in 2001. Yet, it still is one of the bestsellers in the field.

      • Objective - The objective of the book is to explain the usage of Machine learning in various fields like medicine, biology, finance, and marketing. 

      • Edition - The current and revised edition is the 9th edition. Almost all the chapters are revised. 

      • The updated version includes graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. Even a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates, is created.

      • Amazon Kindle offers you a preview of the book. This helps you to decide better if this is the one you are looking for.

      • Rating - The book has received a combined rating of 4-3/5 from a crowd of more than 1100.

      • Format - The formats available on Amazon are Hardcover which costs ₹5,683 and the Kindle edition comes at ₹2522.

      • Amazon Review -
        "Like the first edition, the current one is a welcome edition to researchers and academicians equally... Almost all of the chapters are revised... The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition... If you bought the first edition, I suggest that you buy the second edition for maximum effect, and if you haven't, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" 

      • Level – Beginner
    5. Python Data Science Handbook
      • Intro - Jake VanderPlas is the author of the book. Being is a long-time user and developer of the Python scientific stack, explains the experience and expertise of Jake on the subject.

      • Objective - The objective of the book is giving a perfect start to those who prefer working with the python. This book deals with day-to-day Python-enabled data science. It comprises both the computational and statistical skills required to work with it.

      • The examples will help the learner to analyze, visualize and manipulate the data efficiently.

      • Format - Available in 2 formats in Amazon. The Paperback and the eBook. The former costs ₹ 1250 whereas the latter comes at ₹ 1127. 

      • Good Read offers a preview of the book.

      • Ratings are 4.3/5 from a collection of 157 people.

      • Good Reads Summary - 
    6. Understanding Machine Learning from Theory to Algorithms
      • Intro - As the name goes, the Shai duo has tried their best to make the learners understand Machine learning. 

      • Their approach and variety in the subject make it an idle book for a beginner.

      • Objective - The objective of the book is to introduce machine learning and the algorithmic paradigms it offers, in a principled way. A theoretical account of the fundamentals underlying machine learning is undertaken and the mathematical derivations that transform these principles into practical algorithms.
    7. Preview of the Book on Amazon Kindle.
      • Rating - The Book has revived an Average rating of 4.1/5 from 47 ratings.

      • Format - Available in 3 different formats in Amazon – Paperback, Hardcover, and the Kindle Edition. With costs ₹904, ₹ 4031 and ₹ 858.

      • Edition - The current version is the third edition. 

      • An Amazon review –
        “One of the best books on Machine Learning. In contrast to many other books, this one, as the title says, is aimed at having the reader understand Machine Learning. Machine Learning has become a catchphrase recently and is seeing a lot of activity. However, few make the effort to actually understand the math behind ML in a solid fashion before delving into the various algorithms. That's really a disappointment since ML theory is one of the most elegant theories that you can find (aside from Information theory, which is arguably the most beautiful). This book is intended for those who're able to identify with this spirit and it does a marvelous job at filling the gap.”

      • Level - Beginner
    8. Machine Learning
      • Intro - Published in 1997, this is the most aged book on the list. 

      • The distinguished attribute of the book is that even though its decades old, it features several recent subjects such as genetic algorithms, re-enforcement learning, and inductive logic programming.

      • The most updated edition was released in 2017. 

      • Objective - Tom M. Mitchell’s writing makes it easy and comprehensible for readers. 

      • The best part is, one does not need any experience at all. So, if you don’t know anything about the subject and are looking for a start, Tom’s Machine Learning is your bible.

      • Cost - Available in only Paper Edition which costs ₹ 418.

      • Rating - Having a collective review of 4.1/5 from 76 people in Amazon and 4.05/5 from 654 people in Good reads.

      • Review - A good read review – 

        “Really loved this book! 
        This was my introductory book on how and why machine learning works. I still come back to this book from time to time to serve as a reference point! In my opinion, Tom Mitchell serves up some good motivating examples for the algorithms and simply and clearly explains how they work.”

      • Level - Beginner
    9. Programming Collective Intelligence

      • Intro - The Author of the book is a very popular O'Reilly title, Toby Segaran. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He currently holds the title of Data Magnate at Metaweb Technologies and is a frequent speaker at technology conferences.

      • Objective - The objective of this book is to deal with not only the regular topics of Machine learning but helps you to develop an in-depth understanding of Social media and its algorithms.

      • SEO features such as crawlers, indexers, query engines and the PageRank algorithm are defined as precisely as possible.

      • The foundations of how a computer teaches itself after improving its codes again and again so that the learner has an understanding of intelligent applications.

      • For a better understanding, a preview is available on Amazon.

      • Format - Present in two formats, Kindle and Paperback. The Kindle edition costs ₹ 443, whereas the physical copy of the paperback comes at ₹ 825.

      • Rating - The good reads rating is 4.07/5 from a huge crowd of more than 1350.

        "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
        -- Dan Russell, Google
      • Level – Beginner and Intermediate
    10. Deep Learning
      • Intro - Have you heard of the real-life Tony Stark, Elon Musk? Yes, this is his favorite book on this field.
        "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
        – Elon Musk, CEO of Tesla and SpaceX.

      • Objective - The objective of the book is to introduce Machine learning along with the mathematical aid of Linear algebra, probability theory and information theory and numerical computation has been covered to give a strong foundation and conceptual background

      • The expert trio introduces a variety of topics in Deep learning. Deep learning is the most complicated part of Machine learning because, as the name suggests, it goes in-depth of the subject. But the trio has managed to not only simplify it but with the real-life examples and facts, they have made it interesting too.

      • Cost - The book is available in the only Hardcover in Amazon which costs ₹ 1800.

      • Rating - This book received a rating of 4.4/5 by more than 600 people in Good reads.

      • Level – Advanced
    11. Think Bayes
      • Intro - The only requirements are prior knowledge of Python and Probability. If you have that, then you’re good to dive into Allen’s world of Bayesian Statistics

      • Objective - The objective of the book is to develop Machine learning through codes and techniques of Python to solve Statistical obstructions rather than a mathematical way.

      • Author of the book is Allen B. Downey. He is a Professor of Computer Science at Olin College of Engineering. His heavy experience and wisdom can be seen by the fact that he has taught at Wellesley College, Colby College, and U.C. Berkeley. 
      • The most interesting aspect is that you can apply these learnings to real-life situations like interpreting your SAT scores, simulating kidney tumors, and modeling the human microbiome

      • Cost - Available in 2 formats, Paperback and Kindle Edition. Which costs ₹ 500 and ₹332 respectively.

      • Rating - The book has received a rating of 3.82/5 from 154 people in Good reads.

      • The preview of the book is available on Kindle.
      • A good read review – 
        “Science has been described as simply “a collection of successful recipes”. In “Think Bayes” Allen B. Downey has attempted just that by presenting a set of instructional tutorials for teaching Bayesian methods with Python. In essence, it’s an instructional book with examples that are meant to be straightforward by giving you a simple set of rules in solving more complex sets of problems. The book also makes a few style choices, ignoring continuous distributions in an effort to focus on discrete distributions which make the math more straightforward. Successful recipes need not be complex in every instance, which this book illustrates effectively.”
      • Level - Intermediate
        That concludes the list of top 10 Machine Learning books. Each of them has a specialty. So, pick up any one of them which is suitable and see the world beyond the horizon with Machine Learning.

    It is said that videos are a great way to learn as they are engaging and interactive. They tend to work even better if you’re a visual learner.

    1. MarI/O - Machine Learning for Video Games
      • With a massive reach of 7,681,595 views, this video is one of the best and fun way to get an introduction to Machine Learning from our childhood favorite game, Mario. 
      • The objective here is to show how machine learning can be applied to video games. This is achieved using neural networks and genetic algorithms.
      • It is available on YouTube. 
      • Created by Seth Bling
      • Duration – 6 mins
    2. Machine Learning by Stanford University
      • It is the start of a journey in the series offered by Stanford University. 
      • This class is tutored by none other than, Andrew Ng, former head of the Google Brain.
      • Perhaps, that’s the reason that this video has a view of 2,115,400. 
      • The course is available on Coursera and this video on Youtube.
      • Duration is 1hr 8mins.
    3. The Next generation of Neural Networks
      • Geoffrey Hinton covers the next generation of neural networks. 
      • This is a Google Tech Talk. Developed with the objective to introduce deep learning.
      • Published in 2007, still works great for an introduction.
      • Available on YouTube.
      • Duration is 1 hr.
    4. The Future of Robotics and Artificial Intelligence (Andrew Ng, Stanford University, STAN 2011)
      • Again, comes the former Google Brain head – Andrew Ng. 
      • But this is not entirely on Machine Learning. Rather, through this video Ng. focuses on Artificial Intelligence on Robotics. 
      • Available on Youtube.
      • Duration – 16 mins
    5. Caltech Machine Learning
      • This video is the first step to the series brought to you by Caltech University.
      • Tutored by Professor Yaser Abu-Mostafa.
      • Duration – 1hr 21mins
      • The series also offers an online course. This is a great way to nourish the foundations for Machine Learning.

    That concludes the top 5 videos in Machine learning. You can access all these videos in one place along with 5 more videos in this playlist.

    Moving on the last section – Webinar.

    Webinars or ‘Seminars over the internet’ are a great way to interact and learn new subjects. It involves discussion, problem solving and training. The Ultimate gateway to Understanding a concept.

    Top Webinars to learn Machine Learning 

    1. Learn How to Make Machine Learning Work by KdNuggets
      • This webinar has already taken place in October 2017. But you can still get the videos and documents on demand. That’s the advantage of Webinar over Seminar. 
      • The objective is to fully use machine learning. 
      • This can be done only when one is aware of both the potential benefits and the techniques to create data-driven models.
      • This webinar is suitable for both Beginners and advanced.
    2. A machine learning deep dive by KdNuggets
      • The objective is to learn how to use Databricks on AWS and Snowflake to tackle data science problems.
      • Using multiple languages and machine learning frameworks to drive deep insights into online shopkeeping and learning to make profits.
      • Presented by Michelangelo D'Angostino, Head of Data Science at ShopRunner
      • You can sign up and receive the documents and presentations for the webina
    3. Machine Learning/AI – Facts, Conclusions, and Action.
      • Brought to you by Gartner.
      • Duration – 60 mins
      • The objective is to prepare the learner for the next level into Machine Learning and how to know what is real and what isn’t.
      • Hosted by Tom Austin.
      • You can get the videos and presentations on demand.

    That concludes our webinar section. The thing is, there are several resources available out there. But one cannot go and check out every material and then decided. Sadly, we are all mortals.

    Therefore, to be more productive and learn better, choose the best. 

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    Faqs

    The Course

    Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data.

    Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

    You will:
    • Get advanced knowledge on machine learning techniques using Python
    • Be proficient with frameworks like TensorFlow and Keras

    By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

    There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

    On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

    Your instructors are Machine Learning experts who have years of industry experience.

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