Kind of confused by your categorization. You have machine learning as a category but you're also asking about Bayes, which is an overlapping but also completely different topic.
Anyway, I think Doing Bayesian Data Analysis by John Kruschke is a must read.
Along with Statistical Rethinking by Richard McElreath. The latter book is good for understanding Bayes, but I personally didn't find it a good choice for trying to do Bayesian modelling with real datasets because most of the code in the book is used for the rethinking package, created by the author to drive home important concepts. But it's not the type of scripts you would use in a real world.
This contrasts with the book by Kruschke, which is a lot more comprehensive and covers several topics, especially in experimental settings, and it comes with plenty of applicable scripts. Overall, I'd recommend both books, but I'd recommend Kruschke first and then McElreath to solidy concepts.
However, both books assume that you already have some knowledge about statistics in general (not R, they teach you to use R, especially Kruschke)