I wouldn't say these are level-up but rather some introductory material that covers the basics. Swapping Introduction to Statistical Learning for Elements of S

I wouldn't say these are level-up but rather some introductory material that covers the basics. Swapping Introduction to Statistical Learning for Elements of Statistical Learning is a good step-up if you don't need as much hand-holding (it's essentially the same book, by the same author just more thorough). Then, adding Bishop's ML book is a good idea. Although also introductory, it covers a lot more topics (some kernel methods and probabilistic stuff) and in a more disciplined way. Also, while not that popular in the deep learning hype era, Vapnik's Nature of Statistical Learning is a great read.