Bayesian Machine Learning Explained

This post has been sitting in my bookmarks for a while now - at first I liked the initial part which is very clear and "beginner-friendly" - the best bit, however (and the main reason why I came back to the post a few times now), in my opinion is the "Resources" section at the end, which is really exhaustive.

For Bayesian I recommend PyMC3 (the default version of PyMC is still 2, but 3 is functional and in fast development). Once you know a bit of Bayesian statistics, there is a wonderful tutorial in Jupyter Notebooks: https://github.com/markdregan/Bayesian-Modelling-in-Python

(Also, I've learnt from it the practical difference of fixed priors vs hierarchical priors.)

I get a 503 error on the actual notebook.

Nice post.

For getting started with Bayesian thinking and analytics, I highly recommend "Doing Bayesian Data Analysis_ A Tutorial with R and BUGS" by John Krushke

Seconded. It is the only book I found with a practical, down to earth approach. Highly recommended.

Be sure to get the 2nd edition, "A Tutorial with R, JAGS, and Stan".