Bayesian Machine Learning in Python: A/B Testing

Course details

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 youre a data scientist, and you want to tell the rest of the company, logo A is better than logo B, well you cant just say that without proving itusing numbers andstatistics.

Traditional A/B testing has been around for a long time, and its 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 learningway of doing things.

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

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

Well improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, well improve on both of those by using a fully Bayesian approach.

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

Its an entirely different way of thinking about probability.

Its a paradigm shift.

Youll probably need to come back to this course several times before it fully sinks in.

Its also powerful, and many machine learning experts often make statements about how they subscribe to the Bayesian school of thought.

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

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

Youll learn these fundamental tools of the Bayesian method - throughthe example of A/B testing -and then youll 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!

HARD PREREQUISITES /KNOWLEDGEYOUARE ASSUMEDTOHAVE:

• 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.

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)