Bayesian Machine Learning In Python: A/B Testing
- Likelihood (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, retail, newsfeeds, internet marketing, and extra.
A/B testing is all about evaluating issues.
For those who’re an information scientist, and also you wish to inform the remainder of the corporate, “brand A is healthier than brand B”, properly 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 stuffed with approximations and complicated definitions.
On this course, whereas we are going to do conventional A/B testing with a view to recognize its complexity, what we are going to ultimately get to is the Bayesian machine studying method of doing issues.
First, we’ll see if we will enhance on conventional A/B testing with adaptive strategies. These all enable you remedy the explore-exploit dilemma.
You’ll be taught concerning the epsilon-greedy algorithm, which you will have heard about within the context of reinforcement studying.
We’ll enhance upon the epsilon-greedy algorithm with an identical algorithm known as UCB1.
Lastly, we’ll enhance on each of these through the use of a completely Bayesian method.
Why is the Bayesian methodology attention-grabbing to us in machine studying?
It’s a wholly completely different mind-set about likelihood.
It’s a paradigm shift.
You’ll in all probability want to return again to this course a number of occasions 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 college of thought”.
In sum – it’s going to provide us a number of highly effective new instruments that we will use in machine studying.
The stuff you’ll be taught on this course usually are not solely relevant to A/B testing, however reasonably, we’re utilizing A/B testing as a concrete instance of how Bayesian strategies might be utilized.
You’ll be taught these elementary instruments of the Bayesian methodology – by the instance of A/B testing – and then you definately’ll be capable of carry these Bayesian strategies to extra superior machine studying fashions sooner or later.
See you in school!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
- likelihood (steady and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
- Python coding: if/else, loops, lists, dicts, units
- Numpy, Scipy, Matplotlib
TIPS (for getting by the course):
- Watch it at 2x.
- Take handwritten notes. It will drastically enhance your skill to retain the data.
- Write down the equations. For those who don’t, I assure it can simply appear like gibberish.
- Ask a lot of questions on the dialogue board. The extra the higher!
- Understand that almost all workout routines will take you days or even weeks to finish.
- Write code your self, don’t simply sit there and have a look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Take a look at the lecture “What order ought to I take your programs in?” (accessible within the Appendix 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 strategies to use to their information science work
Created by Lazy Programmer Inc.
Final up to date 10/2018
Dimension: 898.47 MB