machine learning

The ‘Bayesian Machine Learning in Python: A/B Testing’ course will help you to learn the fundamental tools of the Bayesian method through the example of A/B testing and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

The things you’ll learn in this course are not only applicable to A/B testing but rather, they are using A/B testing as a concrete example of how Bayesian techniques can be applied. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Bayesian Machine Learning in Python: A/B Testing’ for INR 449.

Who all can opt for this course?

  • Technically-inclined students and professionals those want to understand Bayesian machine learning methods for use in their data science job

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,79985% off
Duration10 Hours
Student Enrollment34,181 students
InstructorLazy Programmer Inc.
Topics CoveredBayesian A/B testing, machine learning, Data Science, Data Analytics techniques
Course LevelIntermediate
Total Student Reviews5,935

Learning Outcomes

  • To enhance the effectiveness of A/B testing, use adaptive algorithms
  • Recognize the differences between frequentist and Bayesian statistics
  • Analyze A/B testing using Bayesian techniques

Course Content

S.No.Module (Duration)Topics
1.Introduction and Outline (24 minutes)What’s this course all about?
Where to get the code for this course
How to succeed in this course
2.The High-Level Picture (18 minutes)Real-World Examples of A/B Testing
What is Bayesian Machine Learning?
3.Bayes Rule and Probability Review (01 hour 37 minutes)Review Section Introduction
Probability and Bayes’ Rule Review
Calculating Probabilities – Practice
The Gambler
The Monty Hall Problem
Maximum Likelihood Estimation – Bernoulli
Click-Through Rates (CTR)
Maximum Likelihood Estimation – Gaussian (pt 1)
Maximum Likelihood Estimation – Gaussian (pt 2)
CDFs and Percentiles
Probability Review in Code
Probability Review Section Summary
Beginners: Fix Your Understanding of Statistics vs Machine Learning
Suggestion Box
4.Traditional A/B Testing (02 hours 08 minutes)Confidence Intervals (pt 1) – Intuition
Confidence Intervals (pt 2) – Beginner Level
Confidence Intervals (pt 3) – Intermediate Level
Confidence Intervals (pt 4) – Intermediate Level
Confidence Intervals (pt 5) – Intermediate Level
Confidence Intervals Code
Hypothesis Testing – Examples
Statistical Significance
Hypothesis Testing – The API Approach
Hypothesis Testing – Accept Or Reject?
Hypothesis Testing – Further Examples
Z-Test Theory (pt 1)
Z-Test Theory (pt 2)
Z-Test Code (pt 1)
Z-Test Code (pt 2)
A/B Test Exercise
Classical A/B Testing Section Summary
5.Bayesian A/B Testing (02 hours 52 minutes)Section Introduction: The Explore-Exploit Dilemma
Applications of the Explore-Exploit Dilemma
Epsilon-Greedy Theory
Calculating a Sample Mean (pt 1)
Epsilon-Greedy Beginner’s Exercise Prompt
Designing Your Bandit Program
Epsilon-Greedy in Code
Comparing Different Epsilons
Optimistic Initial Values Theory
Optimistic Initial Values Beginner’s Exercise Prompt
Optimistic Initial Values Code
UCB1 Theory
UCB1 Beginner’s Exercise Prompt
UCB1 Code
Bayesian Bandits / Thompson Sampling Theory (pt 1)
Bayesian Bandits / Thompson Sampling Theory (pt 2)
Thompson Sampling Beginner’s Exercise Prompt
Thompson Sampling Code
Thompson Sampling With Gaussian Reward Theory
Thompson Sampling With Gaussian Reward Code
Exercise on Gaussian Rewards
Why don’t we just use a library?
Nonstationary Bandits
Bandit Summary, Real Data, and Online Learning
(Optional) Alternative Bandit Designs
6.Bayesian A/B Testing Extension (18 minutes)More about the Explore-Exploit Dilemma
Confidence Interval Approximation vs. Beta Posterior
Adaptive Ad Server Exercise
7.Practice Makes Perfect (18 minutes)Intro to Exercises on Conjugate Priors
Exercise: Die Roll
The most important quiz of all – Obtaining an infinite amount of practice
8.Setting Up Your Environment (FAQ by Student Request) (37 minutes)Anaconda Environment Setup
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
9.Extra Help With Python Coding for Beginners (FAQ by Student Request) (42 minutes)How to Code by Yourself (part 1)
How to Code by Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
Python 2 vs Python 3
10.Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes)How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Machine Learning and AI Prerequisite Roadmap (pt 1)
Machine Learning and AI Prerequisite Roadmap (pt 2)
11.Appendix / FAQ Finale (08 minutes)What is the Appendix?

Resources Required

  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
  • Numpy stack programming in Python

Featured Review

Julia Perry (5/5) : I’ve found that this instructor’s style of teaching is the way I learn best. Everything is intuitive and built on foundational principles without sacrificing quality. There are plenty of examples and challenges to get you practising. The exercises were really useful and fun to code along with. Well done.


  • Ethan Xu (5/5) : This course finds the perfect balance of theory and coding exercise.
  • Pablo Avendano (5/5) : This was an excellent introduction to A-B testing using Bayesian machine learning.
  • Abhyuday Desai (5/5) : Best instructor if you want to learn the theory and not just the SKLearn APIs.
  • Tariq Azad (5/5) : It’s a very good course to get started with Bayesian machine learning.


  • Adiwid D. (1/5) : A lot of theory is given without links to the proofs so it’s very difficult to really understand the material.
  • Anonymized User (1/5) : If you do not know Python, you will not learn it, if you know Python, you will waste your time.
  • Subhojit Sengupta (1/5) : The coding part is too less and complex, unlike other videos of Udemy.
  • Anonymized User (1/5) : Python videos just show author writing code, with little to no explanation.

About the Author

The instructor of this course is Lazy Programmer Inc. who is a Artificial intelligence and machine learning engineer. With 4.6 Instructor Rating and 148,353 Reviews on Udemy, he/she offers 33 Courses and has taught 526,884 Students so far.

  • Although Instructor have also been recognised as a data scientist, big data engineer, and full stack software engineer, Instructor currently spend the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning
  • Instructor earned his first master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago
  • Instructor’s second master’s degree in statistics with a focus on financial engineering was awarded to him
  • Data scientist and big data engineer with experience in online advertising and digital media (optimising click and conversion rates) (building data processing pipelines)
  • Instructor routinely use big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark
  • Instructor has developed deep learning models for text modelling, image and signal processing, user behaviour prediction, and click-through rate estimation
  • In his/her work with recommendation systems, Instructor used collaborative filtering and reinforcement learning, and they validated the findings using A/B testing
  • Instructor have instructed students at universities like Columbia University, NYU, Hunter College, and The New School in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics
  • Instructor’s web programming skills have helped numerous businesses
  • Instructor handle all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work

Comparison Table

ParametersBayesian Machine Learning in Python: A/B TestingData Science: Modern Deep Learning in PythonData Science: Deep Learning and Neural Networks in Python
OffersINR 455 (INR 2,799) 85% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration10.5 hours11.5 hours12 hours
Rating4.7 /54.7 /54.6 /5
Student Enrollments34,18133,62652,709
InstructorsLazy Programmer Inc.Lazy Programmer Inc.Lazy Programmer Inc.
Register HereApply Now!Apply Now!Apply Now!

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