Definitely do the fast.ai course. Totally worth it.
But also use ISLR, Goodfellow, Bishop, etc.
Start with Andrew Ng's ML, then do the first part of Aurelien Geron book, then do Ng's DL specialization, then do fast.ai. Then learn PyTorch. A great book would be Sebastian Raschka's book. Also d2l.ai. A fast-paced, but really good course would be the Neuromatch DL tutorials.
Then move forward based on your interests.
Yann LeCun has THE best MOOC on DL on YouTube.
For the Math, I majored in Physics, so stuff came naturally. I suggest Imperial London's MOOC on Mathematics for ML specialization, Robert Ghrist's Calculus course, VMLS book for Linear Algebra. For stats, haven’t found a good one yet.
What you read, how much- these all depend on what you want to do. Where do you want to see yourself, and so on.
If you just want to brag about DL and put it on your resume so that you can get a job writing SQL queries and make PowerBI presentation as a "Data Scientist", then the bars are low.
If you want to do some DL, then that is another league altogether.
You need to be able to quickly read papers, understand ideas, use those for your own projects or papers.
