* 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/)
I’ve read all of VMLS and Forecasting (and ISLR from the original list), and maybe half of SLP. MML I have skimmed through for review / used as a reference, and the deep learning book is high on my queue.
I tend to not be a cover-to-cover reader, so I usually deep dive into a single topic for a while (e.g. forecasting, information retrieval) and read papers/tutorials/chapters related to that topic and the math concepts related to it.
PS I feel like impostor syndrome is so common among data scientists because there is so much material that feels like “must know”. Don’t feel like you need to memorize thousands of textbook pages to be effective, and you could spend a lifetime mastering any of these individual subjects. JIT learning is a great skill to have.
* 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/)