[TechDebtDevin]

If I could go back I would start by reading Josh Starmer's Statquest Guide to Machine Learning, and then his guide to AI/Nueral Networks[0]. Starmer does the best job at explaining advanced ML topics in a very beginner friendly way, the books are literally written in the format of a children's book.

Then just start tinkering. I got interested in ML because of sport's analytics and betting markets so I read a lot of papers on that topic and books similar to Bayesian Sports Models in R by Andrew Mack[1].Also, Jake VanderPlas's Python Data Science Handbook is good[2].

Ideally, find a vertical you're interested in where experts have applied ML and read their papers/books and work backwards from there.

[0]: https://statquest.org/statquest-store/ [1]: https://www.goodreads.com/book/show/216487475-bayesian-sport... [2]: https://www.oreilly.com/library/view/python-data-science/978...