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!
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: ab_testing
Make sure you always "git pull" so you have the latest version!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
- calculus
- probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy, Scipy, Matplotlib
TIPS (for getting through the course):
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don't, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don't just sit there and look at my code.
USEFUL COURSE ORDERING:
- (The Numpy Stack in Python)
- Linear Regression in Python
- Logistic Regression in Python
- (Supervised Machine Learning in Python)
- (Bayesian Machine Learning in Python: A/B Testing)
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- (Supervised Machine Learning in Python 2: Ensemble Methods)
- Convolutional Neural Networks in Python
- (Easy NLP)
- (Cluster Analysis and Unsupervised Machine Learning)
- Unsupervised Deep Learning
- (Hidden Markov Models)
- Recurrent Neural Networks in Python
- Artificial Intelligence: Reinforcement Learning in Python
- Natural Language Processing with Deep Learning in Python
- Advanced AI: Deep Reinforcement Learning in Python
- Students and professionals with a technical background who want to learn Bayesian machine learning techniques to apply to their data science work
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