🤖[NEW COURSE CYBERWEEK] Data Science: Bayesian Classification in Python

Hello friends!

Do your eyes deceive you? A new course so soon? Yes! It’s true!

Don’t want to read my spiel? Just get the course here:


This course kind of happened by accident.

I just finished the Naive Bayes course and prior to that Bayesian Linear Regression. Note: they are not related other than the term “Bayes” in the title.

But this turned out to be essentially the perfect setup for this course: Bayesian Classification.

How so?

This course takes the Bayes Classifier and makes it “Bayesian” (i.e. using the techniques you learned in Bayesian Linear Regression, such as computing the posterior predictive distribution).

But it also happens to make heavy use of Bayes rule (you’ll see it everywhere in the course, trust me on this) in the same way that it’s used in Naive Bayes.

So, it’s basically the perfect sequel if you registered for both of those courses (note: Naive Bayes is a simple enough model that you could have just learned it yourself elsewhere, but you’ll really want to know the material from Bayesian Linear Regression).


Not as heavy as Bayesian Linear Regression since that course already introduced all the essential techniques. The math in this course is more conceptual / intuitive (as long as you meet the prerequisites) and focuses more on implementation.

Implementing the algorithms you learn about is the really fun part of this course.

Sidenote: Since so many people ask for PDF notes, I’ve decided to try something new which is to use LaTeX to generate PDF notes (instead of slides), and I’ve also used mostly handwritten derivations due to the positive feedback from Bayesian Linear Regression.

Sidenote 2: This is a deeplearningcourses.com exclusive. It is simply TOO HOT for Udemy. Seriously though, I don’t think the average Udemy student could handle it.

So what are you waiting for?