What you would learn in Bayesian Machine Learning in Python: A/B Testing course?
This class is focused on Testing A/B.
A/B testing is utilized everywhere. Retail, marketing, newsfeed advertising on the internet, and much more.
A/B testing is about comparing items.
When you're a data analyst and want to tell everyone else in the company, "logo A is better than logo B, " you should not just state that without supporting it with numbers and data.
The traditional A/B test has been used for a long time and is full of estimates and confusing definitions.
In this course, even though we'll do the traditional A/B tests to comprehend its complicated nature, what we'll eventually arrive at will be the Bayesian machine-learning method of conducting things.
We'll first determine whether we can improve the traditional A/B test using flexible methods. All of this help solve the issue of exploring and exploiting.
You'll be taught about the Epsilon-greedy algorithm, which you've probably seen about reinforcement learning.
We'll enhance the epsilon-greedy algorithm using an identical algorithm called UCB1.
We'll also improve both of them by using the complete Bayesian method.
What makes the Bayesian method of learning enjoyable to machine learning researchers?
It's an entirely different approach to thinking about probabilities.
It's an evolution in paradigms.
You will likely be required to repeat this lesson multiple times before it completely takes hold.
It's also mighty. It's also incredibly effective, and machine learning experts announce that they "subscribe to the Bayesian school of thought."
In short, it will provide us with many powerful new tools to improve machine learning.
The lessons you'll learn in this course aren't just applicable to A/B testing. However, we'll use A/B testing to illustrate how Bayesian strategies can be used.
You'll be taught the basic techniques of the Bayesian method through examples of testing A/B, and you'll then be able to carry these Bayesian techniques into a more advanced machine learning model in the coming years.
- Utilize adaptive algorithms to enhance the performance of A/B testing
- Learn the difference between Bayesian and frequentist statistics.
- Use Bayesian methods for A/B testing.
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