It has a chapter on optimization that is left out in most books but skips statistics. I also chanced upon Mathematics for Machine Learning book recently and it seems to be good. Communities like r/AskStatistics and r/statistics on Reddit were really helpful when I got stuck. has a couple of chapters at the beginning that gives you a fairly good idea of the mathematics required to get into Deep learning.

The Deep learning book by Ian Goodfellow et al. StatQuest channel on Youtube has handwavy but easy to understand videos on statistics for ML too. AP® Statistics is all about collecting, displaying, summarizing, interpreting, and making inferences from data. I read probability from Mathematics for Computer Science-MIT and also referred Khan Academy and PennState STAT 414/415 for statistics and probability. If you have the time, you could look up Coding the matrix too. Strang is really good and is designed for self-study.

MIT OCW Scholar(independent study) course on Linear Algebra by Prof. You should take up books if you are serious.

The first two courses weren't rigorous enough for my taste (I am yet to find a rigorous course on Coursera), but the third was pretty good. It was a good supplement to other resources, especially 3blue1brown's Linear Algebra course on youtube (mind-blowing, do check it out) but I wouldn't recommend it as a first course. I have completed all three courses in the series.

Khan academy statistics and probability