* Vectors, Matrices, and Least Squares — IMO the best beginner-friendly and applications-focused intro to (or review of) linear algebra. Covers a ton of fundamental ground while keeping things consistent and concise. Lots of exercises and a Julia supplement book. (http://vmls-book.stanford.edu/)
* Mathematics for Machine Learning — good coverage of the most important math concepts relevant to ML (https://mml-book.github.io/)
* Forecasting: Principles and Practice — best overall resource on forecasting that I know of; R focus. One of a zillion great R/data science books, virtually all of which are open and well-written. (https://otexts.com/fpp2)
* Dive Into Deep Learning — can’t personally vouch for this one but it looks comprehensive; numpy/PyTorch/TF focus (https://d2l.ai/)
* Speech and Language Processing — clear introduction to all things NLP, nice flow (https://web.stanford.edu/~jurafsky/slp3/)