More free books:

* 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/)

Lack of math knowledge won't limit much as a practitioner, but will definitely make it harder for you to understand what is going on.

Luckily, the math involved is not so difficult. For deep learning specifically you should be comfortable with linear algebra and multivariate calculus, and for machine learning in general you should be familiar with probabilistic thinking.

"Mathematics for machine learning" [1] is a good introduction to these topics.

[1] https://mml-book.github.io/

Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong.

https://mml-book.com/

"Mathematics for Machine Learning" by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong.

https://mml-book.github.io/

This book is aslo Mathematics for Machine Learning: https://github.com/soulmachine/machine-learning-cheat-sheet

Mathematics for Machine Learning - https://mml-book.github.io/

You should consider taking this course series "Mathematics for Machine Learning" from coursera.
https://www.coursera.org/specializations/mathematics-machine...

However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math.
It'll help you see where the math you're learning is actually used, as you learn them.

Try deeplearning.ai first, then try this "math for ML" course.