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...

I've been planning to go from "can work it out with copious examples" to "knows when to use apply, transform, etc. off the cuff" level of knowledge in Pandas - do you have any suggestions on good resources on this?

I'd like to understand better why the data structures work the way they do and thus have an intuition on what operations to use when. The O'Reilly Python Data Science Handbook[0] seems like it might be useful here, but I'm not sure if it is still up to date.

[0] https://www.oreilly.com/library/view/python-data-science/978...