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Bayesian Machine Learning in Python: A/B Testing Download

Knowledge Science, Machine Learning, and Knowledge Analytics Methods for Advertising and marketing, Digital Media, On-line Promoting, and Extra

Bayesian Machine Learning in Python AB Testing
Bayesian Machine Learning in Python AB Testing
What you’ll be taught
  • Use adaptive algorithms to enhance A/B testing efficiency
  • Perceive the distinction between Bayesian and frequentist statistics
  • Apply Bayesian strategies to A/B testing
  • Chance (joint, marginal, conditional distributions, steady 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 in all places. Advertising and marketing, retail, newsfeeds, internet marketing, and extra.

A/B testing is all about evaluating issues.

Should you’re a knowledge scientist, and also you wish to inform the remainder of the corporate, “logo A is better than logo B”, effectively you possibly can’t simply say that with out proving it utilizing numbers and statistics.

Conventional A/B testing has been round for a very long time, and it’s filled with approximations and complicated definitions.

On this course, whereas we’ll do conventional A/B testing in order to understand its complexity, what we’ll finally get to is the Bayesian machine studying means of doing issues.

First, we’ll see if we will enhance on conventional A/B testing with adaptive strategies. These all aid you remedy the explore-exploit dilemma.

You’ll be taught in regards to the epsilon-greedy algorithm, which you will have heard about in the context of reinforcement studying.

We’ll enhance upon the epsilon-greedy algorithm with the same algorithm referred to as UCB1.

Lastly, we’ll enhance on each of these through the use of a totally Bayesian method.

Why is the Bayesian technique fascinating to us in machine studying?

It’s a completely totally different mind-set about chance.

It’s a paradigm shift.

You’ll in all probability want to come back again to this course a number of instances earlier than it absolutely sinks in.

It’s additionally highly effective, and lots of machine studying specialists typically make statements about how they “subscribe to the Bayesian school of thought”.

In sum – it’s going to present us plenty of highly effective new instruments that we will use in machine studying.

The belongings you’ll be taught in this course will not be solely relevant to A/B testing, however reasonably, we’re utilizing A/B testing as a concrete instance of how Bayesian methods may be utilized.

You’ll be taught these elementary instruments of the Bayesian technique – by way of the instance of A/B testing – and then you definately’ll be capable of carry these Bayesian methods to extra superior machine studying fashions in the long run.

See you in class!

“If you can’t implement it, you don’t understand it”
  • Or as the good physicist Richard Feynman mentioned: “What I cannot create, I do not understand”.
  • My programs are the ONLY programs the place you’ll learn to implement machine studying algorithms from scratch
  • Different programs will train you plug in your knowledge right into a library, however do you really want assist with 3 strains of code?
  • After doing the identical factor with 10 datasets, you understand you didn’t be taught 10 issues. You realized 1 factor, and simply repeated the identical 3 strains of code 10 instances…


Advised Conditions:
  • Chance (joint, marginal, conditional distributions, steady and discrete random variables, PDF, PMF, CDF)
  • Python coding: if/else, loops, lists, dicts, units
  • Numpy, Scipy, Matplotlib



  • Take a look at the lecture “Machine Learning and AI Prerequisite Roadmap” (obtainable in the FAQ of any of my programs, together with the free Numpy course)
Who this course is for:
  • College students and professionals with a technical background who wish to be taught Bayesian machine studying methods to use to their knowledge science work
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