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Bayesian Machine Learning in Python: A/B Testing

(12 customer reviews)
Product is rated as #19 in category Data Science

What you’ll 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

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!

“If you can’t implement it, you don’t understand it”

  • Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
  • After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…


Suggested Prerequisites:

  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy, Scipy, Matplotlib



  • Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

  • Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work

12 reviews for Bayesian Machine Learning in Python: A/B Testing

3.8 out of 5
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  1. Ross Weijer

    a bit easy from both python and stats perspective, but it does a good job of giving you enough theory and enough set up to implement with simulated data

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  2. Arundhati Verma

    The course is detailed & well prepared. It is in depth enough for a successful impact at work. The instructor has a strong passion about the subject, it is motivating me to learn more.

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  3. Inchan Hwang

    I have been through more than half of the course materials. By now, I can confidently tell you that Lazy programmer would not let you down for his courses. It is one of few courses over Udemy that I can recommend anyone to watch. I am sure you will learn something out of his course materials.

    By the way, Does Lazy programmer offer other courses on Bayesian statistics? at least somewhat related with Bayesian statistics?

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  4. Roberto Arce

    I didnt really learn, the guy is doing a good job at bringing the theory. But it does not manage to explain to a regular non person.
    If i did not know before hand i would not be able to understand anything.

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  5. Amir M. Ostad

    Section 5 is very poorly done! You won’t understand most of section 5 not because it’s difficult of any sort but rather the poor explanations and horrible programming technique the instructor uses. In Epsilon-Greedy code, he doesn’t even explain whys of the code. Why have you written it this way? What does each variable mean? Terrible!
    The other sections are OK!

    I think he assumes a lot of thing are already known by the viewer.
    I had to use Wikipedia and my books a lot to compensate for his poor lectures! And then he refuses to share the slides too which makes the whole learning process more bumpy.
    I personally won’t buy anything from this Lazy whatever again.

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  6. Alexander Golan

    Great. I’ve learned a lot and already got several interviews thanks to the course. I’ve recommended it to my friends and colleagues and they are having success also. THANK YOU!

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  7. Robert Guardado

    Lazy Programmer is extremely detail oriented. He provides a lot of theory and examples as he is presenting the topics and does not rush through anything, helping to ensure all students understand the message.

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  8. Adam Musa

    This is an extremely good course. I thought I knew even the basics of statistics. Mr Lazy Programmer does an intense but easy to follow simple to advanced progression in this course. He shows you the true power of A/B testing and Bayesian machine learning. Highly recommend to data scientists looking for additional skills.

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  9. RiverSong

    I feel like not enough was done to establish an intuition for beta distributions and selecting different posteriors.

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  10. Mario Luis

    Very hard course but very well instructed. I have got some certificates already from other courses from Lazy Programmer and they are all taken very seriously by the instructor, it is like a real university classroom course, exercises are hard, you have to rewatch a lot sometimes to understand the content clearly but then you get it and you can do the exercises. You must dedicate enough time on it… In the end you feel that you understand the subject and it will naturally pop up on your head when you think of an application where it may be useful to solve the problem…

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  11. Xiaodan Zhong

    I’m an MBA transitioning into the ML world, I have little previous knowledge and I’m following the course with ease. I’m extremely happy with the way Lazy Programmer explains things, only wish I’d brushed up on my math haha. I’m completely satisfied with the course. Great upgrade in skills for any business, finance, or econ major, and developers and data scientists.

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  12. Hkbu-Davidlo

    A concise course overall. The course appears to be updated some time ago, which is good, but connections between some new and old lectures have been lost.

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


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