tl;dr Is the following path (with the following books) the correct way to learn Mathematics for Machine Learning? 0. Pre-algebra (Pre-Algebra Essentials For Dummies by Mark Zegarelli, Krista Fanning)
- College Algebra (Schaum’s Outline of College Algebra by Robert E. Moyer, Murray R. Spiegel)
- Pre-calculus (Schaum’s Outline of Precalculus by Fred Safier)
- Calculus (Schaums Outline of Calculus by Frank Ayres, Elliott Mendelson)
- Differential Equations (Schaum’s Outline of Differential Equations by Richard Bronson, Gabriel B. Costa)
- Discrete Mathematical Structures (Schaums Outline of Discrete Mathematics by Seymour Lipschutz, Marc Lipson)
- Linear Algebra (Introduction to Linear Algebra by Gilbert Strang)
- Statistics and Probability (Practical Statistics for Data Scientists 50+ Essential Concepts Using R and Python by Peter Bruce, Andrew Bruce, Peter Gedeck)
Here's the lengthier version of my query. I was never bad with studies, however, I got bad grades and just passed my college because of some personal issues. I have been working since then in jobs which didn't require much mathematics.
I have switched careers now and I am working as a Machine Learning Engineer now. I did a course in Machine Learning but skipped the mathematics part. I want to study mathematics now, not just because it is required at work, but because I was always fascinated by mathematics. The issue is, I put in 60 hours a week in office. I take out time to study after work. So I want to know if these books would actually help me understand the mathematics behind Machine Learning, or these will not cover it? Thank you for reading through this, any suggestion/help is highly appreciated.
I would probably switch 'Differential Equations' with a book on optimization theory. I haven't read any of the books you mention, but judging from the titles they seem to be highly relevant.
I would also like to recommend the following (free) book, which seems to be just what you are looking for. Mathematics for Machine Learning.
https://mml-book.github.io/
It covers a lot of the topics you mention and it also has a section in every chapter with recommended further readings.