Machine Learning with Python Certification

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
  • 350000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Machine Learning with Python skills

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

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Highlights

  • 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

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Why Machine Learning with Python?

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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 the complete machine learning course with Python skills and meet that need.

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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

Prerequisites for Machine Learning With Python

  • 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

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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 Machine Learning with Python, including 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.

Skills 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

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Machine Learning Course with Python Curriculum

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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  

Topics

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

Hands-on

  • 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 

Topics

  • 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 Applied Machine Learning in Python 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 

Topics

  • 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

Topics

  • 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

Topics

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

Hands-on

  • 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  

Topics

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

Hands-on

  • 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 

Topics

  • 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

Hands-on

  • 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 

Topics 

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

Hands-on

  • 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 certification Course is one of the best machine learning with python courses. This course 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 

Learn Machine Learning with Python through a curriculum 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 practical machine learning with python 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 Machine Learning with Python syllabus 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 learners through machine learning algorithms in python, 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, all in all making it the best machine learning with python course. 

Yes, our Machine Learning with Python certification 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. 

The complete Machine Learning Course with Python requires daily training hours. In addition to the training hours, we recommend spending about 2 hours every day, for the duration of the course.   

Machine Learning course with Python 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 Machine Learning with Python certification course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the complete 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 advanced Machine Learning with Python 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. 

Our introduction to Machine Learning with Python course will give you an 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

Learn Machine Learning with Python at KnowledgeHut which 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 with Python certification course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, python machine learning projects, 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 support@knowledgehut.com and we will be happy to get back to you. 

Additional FAQs on the Machine Learning with Python Training

Introduction To Machine Learning with Python

Machine Learning and AI have taken the centre-stage as more and more brands realise the possibilities of these tools in the post-COVID world. The demand for data engineers was up 50% and the demand for data scientists was up 32% in 2020 compared to the prior year.  

Some of the benefits of learning machine learning with python include the following:  

  • Validates your machine learning skills needed for improving career opportunities  
  • Improved potential for a better salary  
  • Expanded knowledge base  
  • It reels in better job opportunities  
  • Machine Learning engineers earn a pretty penny  
  • Demand for Machine Learning skills is only increasing  
  • Most of the industries are shifting to Machine Learning  

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.  

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:  

  • Medical diagnosis: Cancer cell detection, gene printing etc.  
  • Information retrieval: Search engines including both text as well as image search.  
  • Natural Language processing: Recommendation systems, photo tagging, and sentiment analysis.  
  • Finance: Stock predictions, trading, financial banking decisions.  
  • Computer vision: Real-time facial recognition, augmented reality, self-driving cars.  
  • Customer experience: Chatbots.  
  • Languages: Translations of text as well as images, language recognition of different dialects.  

To complete a Machine learning with Python project:  

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

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

  • Programming languages: Being comfortable working with programming languages such as Python, Java, Scala etc  
  • 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.  
  • Machine Learning visualization tools: A basic knowledge and understanding of some of these tools will turn out to be helpful while you are applying the concepts of Machine Learning in real life.  
  • 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.  

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

You do not need a PhD in order to learn Machine learning with Python 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.  

The complete machine learning course with python involves the fundamentals of machine learning and Python that will help you accelerate your career as a Data science practitioner. At the end of this machine learning with python training, 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.  

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. 

Learn Machine Learning

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 SimplyHired.com, the average income of a machine learning engineer is pegged at $142,000, whereas an experienced machine learning engineer can earn up to $195,752 per year.
  3. Demand for Machine Learning skills is only increasing: There exists a huge gap between the demand and the availability of Machine Learning engineers. This skill gap is increasingly being lamented by the Chief Information Officers (CIOs) of some of the biggest corporations around the world. What this means, essentially, is that the demand as well as the pay for professionals with Machine Learning skills is only going to increase in the future.
  4. Most of the industries are shifting to Machine Learning:  Most industries in operation around the world are dealing with a humongous amount of data that is only increasing every single day. The benefits reaped by a thorough analysis of this data is a fact that companies are fast taking cognizance of. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors.
    At such as time, all industries from the financial services sector to government agencies, the healthcare industry and oil and gas mega-corporations to the transportation sector - every industry is a ripe field for work in the field of Machine learning.

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

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

Machine Learning Algorithms

  • 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.  
  • 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.  
  • 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.  

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. 

Machine Learning Courses

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. 

You do not need a PhD in order to learn Machine learning with Python 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.  

The complete machine learning course with python involves the fundamentals of machine learning and Python that will help you accelerate your career as a Data science practitioner. At the end of this machine learning with python training, 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.

Machine Learning with Python

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. 

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 advanced machine learning with python 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, making it one of the best machine learning with python course around the world.

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.  

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 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.

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.

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.

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 Course:

  • 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.

To complete a Machine learning with Python 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 Books and Videos

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

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

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

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

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

Moving on the last section – Webinar.

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

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

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

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

What learners are saying

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Shifa Al Kiyumi RTO Engineer
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A useful course, I acquired knowledge about Python, Machine Learning Modeling Flow, Treating Data, Statistical Learning and other topics. I will use this training and even the recorded videos and materials from knowledge Hut for future projects.

Attended Machine Learning with Python workshop in July 2021

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The Backend boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

Attended Front-End Development Bootcamp workshop in July 2021

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The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could. 

Attended Front-End Development Bootcamp workshop in July 2021

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The learn by doing and work-like approach throughout the bootcamp resonated well. It was indeed a work-like experience. 

Attended Back-End Development Bootcamp workshop in May 2021

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I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

Attended Back-End Development Bootcamp workshop in April 2021

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Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.

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Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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