Machine Learning Course with Python

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 250,000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Machine Learning skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

..... Read more
Read less


  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Accredited by

Why Machine Learning?


Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand machine learning and Python skills and meet that need.  

..... Read more
Read less

Not sure how to get started? Let our Learning Advisor help you.

Contact Learning Advisor

The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.


Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

100% Money Back Guarantee

Can't find the batch you're looking for?

Request a Batch

What you will learn in the Machine Learning with Python course


Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.


Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.


Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.


Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.


Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.


Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .


Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis.

Skill you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

Transform Your Workforce

Harness the power of data to unlock business value

Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.  

  • Custom Training Solutions. 
  • Applied Learning.
  • Learn by doing approach.
  • Get in touch for customized corporate training programs.
500+ Clients

Machine Learning with Python Training Curriculum

Download Curriculum

Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  


  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  


  • Learning to implement statistical operations in Excel

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of 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 with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 


  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling relates to Machine Learning 


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

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp


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

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp


  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study


  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use 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 based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  


  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study


  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 


  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study


  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 


  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study


  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on and we will be happy to get back to you. 

Additional FAQs on the Machine Learning with Python Training

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 the 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, 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 way to get started on Machine Learning as a beginner includes following 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 difficult and you can do it if you have the passion
    • 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 shaping 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
    • Focusing 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: Being comfortable working 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, 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 used in Machine Learning. 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 encyclopedia 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.

Here are some tips that will help you select the best machine learning course:

  • It should be career-focused - You will only be able to get the benefit from an ML course if it is focused on projects that can help you learn how to apply machine learning in real life. For example, predictive models, a phishing website, etc.
  • It should cater to your budget and time. You need to find a course that offers multiple schedules and is under your budget.
  • The Machine Learning course you are going to pursue should be flexible. It must be available online so that you can study from anywhere, whether you are at home, work or on vacation.
  • The course must have highly engaging trainers who are knowledgeable and experienced. Also, they must have the required industry experience to help you get practical learning experience.

Python is a favoured programming language among the developers. From scripting to web development to process automation to machine learning, it is used everywhere. If you want to study Machine Learning, you must have the knowledge of the fundamentals of the language and its libraries like the following:

  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn

Here is how you can get a machine learning job with no experience:

  • Experience - No company is going to hire someone who has no experience with Machine Learning. But this experience doesn’t have to be with a company.
    Here are some other ways to get experience:
    • Personal Projects - Your GitHub must have ML projects. This will be one of the first things recruiters look at. You don’t have to work on big projects. Small projects help show that you have an understanding of the concept.
    • Hackathons - At hackathons, you will be forced to build something and meet experienced people.
    • Coding Challenges - Through coding challenges, you will need to build applications of what you have learned.
    • Open-source projects - These projects have a real insight into the production level code. You will also learn valuable skills like debugging, developing with other people, versioning control, and machine learning.
  • Understand the theory - This is more than just studying some tutorials. You must understand what you are building. You can try getting a Machine Learning certification from an accredited training provider.
  • Specialization - Machine Learning is a vast field. A single person can’t know everything. If you can show the recruiters that apart from a solid understanding of Machine Learning, you also are specialized in one field, you will become more valuable. Here are some specializations you can go for:
    • Computer Vision
    • Generative Adversarial Networks
    • Meta-Learning
    • Natural Language Processing
    • Neural Network Debugging and Visualization
    • One-Shot Learning
    • Recurrent Networks
    • Reinforcement Learning

In theory, you can learn about Machine Learning in 3 to 4 months. However, it will take you more than 6 months to practice and get a good grip. Even if the course you have selected is just for 3 months, you need to keep on practicing after the course to become an expert in ML.

If you are learning Machine Learning from scratch, you need to start with Maths. You must have an understanding of the following 3 concepts:

  • Linear Algebra
  • Probability and Statistics
  • Calculus (Derivatives, Partial Derivatives, and Integration)

Once you are done with Mathematics, you can start with the following:

  • Python
  • Machine Learning Algorithms with Python Libraries
  • Deep Learning Algorithms with Python Libraries
  • Solve case studies using standard datasets

Another option you have for learning Machine Learning Scratch is to enrol yourself in a Machine Learning training program offered by a reputed training provider like KnowledgeHut.

The cost of the KnowledgeHut’s Machine Learning course in India is between INR 42999. In the US, the cost of course is $2199.

Yes, Machine Learning certificates are worth it. Here are the reasons why: 

  • Validates your machine learning skills needed for improving career opportunities
  • Improved potential for a better salary
  • Expanded knowledge base

The best way to learn machine learning is by practicing it. There are several tutorials available online and offline that you can use. But you need hands-on training to learn how to solve problems. Here are a few online platforms you can use for practicing computational applications:

  • CloudX Lab - This is an online cloud platform that offers online video courses, assessment tests, 24 hours lab access with Jupyter environment, and BootML - a UI-based ML model code generator. They offer separate packages for lab enrolment.
  • Google Colab - This is built on the Jupyter Notebook environment and runs on Google Cloud Platform. It provides a GPU that is free of cost and supports Python 2 and 3. Using Colab, you can work on your machine learning coding skills and learn how to develop deep learning applications.
  • Kaggle - This is a customizable, no setup, Jupyter Notebook environment offered by Google. It also offers free GPUs. It also has about 200,000 public notebooks and 19,000 public datasets that can help in practicing data science computational problems.
  • MachineHack - This online platform by the Analytics India Magazine can be used for testing and practicing machine learning skills. You can easily learn and practice ML algorithms like Linear Regression, Support Vector Regression, Multiple Linear Regression, Naive Bayes, K-Nearest Neighbours, Extreme Gradient Boosting Classification, etc.
  • OpenML - This is an open, automated, and collaborative ML environment that has specific features like finding or adding data to analyze, downloading, or creating computational tasks, etc. It is a cross-platform programming environment where you can share and organize data, and experiment with ML algorithms.

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 neighbor 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 neighbors. 
  • 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 neighboring 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 neighbors 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 neighbors 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 regressions 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 unlabeled 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
    • Linear Regression -  The relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx
    • 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. 
    • 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).
    • 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. 
    • 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 -
    • 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.
    • 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.
    • 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 
    • 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.
    • 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 into 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. Human 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 hardware 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 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 extensive code to help participants 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.

We at Knowledgehut, conduct Machine Learning training in all the cities across the globe and here are a few listed for your reference:

New Jersey
Kuala Lumpur

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 higher will be 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 and 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 and 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, and 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.

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

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

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. 

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. 

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.

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. 

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    

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. 

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.

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

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

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 and 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

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

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

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

7. Data Science, Deep Learning, and 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

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

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

4. Machine Learning Training A-Z™: Hands-On Python and 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 and 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

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

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.

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

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.

You 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.

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
  1. 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.
  2. 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. 
  3. 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.
  4. 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 focused 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.plot(data[:, 2])
    • plt.xlabel('Table Index')
    • plt.ylabel(headers[2])

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 and 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
  • 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 thefollowing 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
    • 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”, 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.
  • 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.