Bayesian Machine Learning In Python: A/B Testing is a type of machine learning that uses Bayes theorem to make predictions. It is used to test the effectiveness of different marketing campaigns. A/B testing is a technique used in marketing to compare the results of two different versions of a website or advertisement. The idea is to test which version of the site or advertisement is more effective in converting users into customers.

There are a few steps that need to be followed in order to conduct an A/B test: 1) Choose a test group 2) Choose two different versions of the site or advertisement 3) Determine which version converts more users into customers 4) Compare the results

1) Choose a test group- The test group will be the people who are going to be using the different versions of the site or advertisement. 2) Choose two different versions of the site or advertisement- The two different versions of the site or advertisement can be either a control version (the version that is not being tested) and an experimental version (the version that is being tested). 3) Determine which version converts more users into customers- In order to determine which version converts more users into customers, you need to measure how many users visit the control version and how many users visit the experimental version, and then compare the results. 4) Compare the results- Once you have determined which version converts more users into customers, you can compare the results to see if there is a difference.

- Use adaptive algorithms to improve A/B testing performance
- Understand the difference between Bayesian and frequentist statistics
- Apply Bayesian methods to A/B testing

### Bayesian Machine Learning in Python: A/B Testing Course Requirements

- Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
- Python coding with the Numpy stack

**Bayesian Machine Learning in Python: A/B Testing Course 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)

Recommended Course: AWS Solution Architect Associate Step by Step – 2021

## 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

Source: https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/

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