[jointpdf]

A couple recommendations piggybacking off of yours:

A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home

Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/

There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learnin...

[disgruntledphd2]

Also, Regression and Other Stories is the new edition of the Regression with Multilevel models book, and it's much, much better (especially for n00bs).