bestcourses is supported by learners. When you buy through links on our website, we may earn an affiliate commission. Learn more

Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

4.65 / 5.0
30083 students10 hours 21 minutes

Created by Lazy Programmer Inc., offered on Udemy

bestcourses score™

Student feedback

8.9/10

To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 30083 students which left 5339 reviews at an average rating of 4.65. Impressive!

Course length

9/10

We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 10 hours 21 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.

Overall score

8.9/10

This course currently has a bestcourses score of 8.9/10, which makes it a great course to learn from. On our entire platform, only 15% of courses achieve this rating!

Description

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


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

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

What you will 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

Requirements

  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
  • Python coding with the Numpy stack
Udemy logo
Available on

Udemy

With almost 200,000 courses and close to 50 million students, Udemy is one of the most visited online learning platforms. Popular topics include software development, the digital economy, but also more traditional topics like cooking and music.

Frequently asked questions

  • Price: $94.99
  • Platform: Udemy
  • Language: English
  • 10 hours 21 minutes
Bayesian Machine Learning in Python: A/B Testing thumbnail

bestcourses score: 8.9/10

This course is better than many others in its category.