Bayesian Machine Learning in Python: A/B Testing
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Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More
4.6 (282 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
4,189 students enrolled
Last updated 5/2017
Current price: $15 Original price: $120 Discount: 88% off
30-Day Money-Back Guarantee
  • 3.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Use adaptive algorithms to improve A/B testing performance
  • Understand the difference between Bayesian and frequentist statistics
  • Apply Bayesian methods to A/B testing
View Curriculum
  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
  • Python coding with the Numpy stack

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we’ll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It’s an entirely different way of thinking about probability.

It’s a paradigm shift.

You’ll probably need to come back to this course several times before it fully sinks in.

It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.

You’ll learn these 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.

See you in class!

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: ab_testing

Make sure you always "git pull" so you have the latest version!


  • calculus
  • probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don't just sit there and look at my code.


  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Artificial Intelligence: Reinforcement Learning in Python
  • Natural Language Processing with Deep Learning in Python
  • Advanced AI: Deep Reinforcement Learning in Python

Who is the target audience?
  • Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work
Curriculum For This Course
40 Lectures
Introduction and Outline
3 Lectures 07:01
Bayes Rule and Probability Review
8 Lectures 45:26
Bayes Rule Review

Simple Probability Problem

Maximum Likelihood - Mean of a Gaussian

Maximum Likelihood - Click-Through Rate

Confidence Intervals

What is the Bayesian Paradigm?
Traditional A/B Testing
12 Lectures 53:25
A/B Testing Problem Setup

Simple A/B Testing Recipe


Test Characteristics, Assumptions, and Modifications

t-test in Code

0.01 vs 0.011 - Why should we care?

A/B Test for Click-Through Rates (Chi-Square Test)

CTR A/B Test in Code

A/B/C/D/… Testing - The Bonferroni Correction

Statistical Power

A/B Testing Pitfalls

Traditional A/B Testing Summary
Bayesian A/B Testing
10 Lectures 48:42
Explore vs. Exploit

The Epsilon-Greedy Solution


Conjugate Priors

Bayesian A/B Testing

Bayesian A/B Testing in Code

The Online Nature of Bayesian A/B Testing

Finding a Threshold Without P-Values

Thompson Sampling Convergence Demo

Confidence Interval Approximation vs. Beta Posterior
Practice Makes Perfect
3 Lectures 10:25
Exercise: Compare different strategies

Exercise: Die Roll

Exercise: Multivariate Gaussian Likelihood
4 Lectures 45:09
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

About the Instructor
Lazy Programmer Inc.
4.6 Average rating
11,464 Reviews
60,879 Students
18 Courses
Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.

I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.