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Comment by jamestimmins | Hacker News Reader

On a related note, I'm curious if anyone has taken the Mathematics for Machine Learning (https://www.coursera.org/specializations/mathematics-machine...) courses on Coursera, and whether it really covers enough to be comfortable with ML. The course bills itself as enough math knowledge for folks who barely remember high school math.

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Looking at the courses, it doesn't cover calculus and probability. That's two topics that you should already know and that you might not have mastered in High School. Otherwise, you are good to go.

I have completed all three courses in the series. It was a good supplement to other resources, especially 3blue1brown's Linear Algebra course on youtube[0] (mind-blowing, do check it out) but I wouldn't recommend it as a first course. 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. You should take up books if you are serious.

MIT OCW Scholar(independent study) course on Linear Algebra by Prof. Strang[1] is really good and is designed for self-study. If you have the time, you could look up Coding the matrix[2] too. I read probability from Mathematics for Computer Science-MIT[3] and also referred Khan Academy[4] and PennState STAT 414/415 [5] for statistics and probability. StatQuest channel[6] on Youtube has handwavy but easy to understand videos on statistics for ML too. The Deep learning book[7] by Ian Goodfellow et al. has a couple of chapters at the beginning that gives you a fairly good idea of the mathematics required to get into Deep learning. Communities like r/AskStatistics and r/statistics on Reddit were really helpful when I got stuck.

I also chanced upon Mathematics for Machine Learning[8] book recently and it seems to be good. It has a chapter on optimization that is left out in most books but skips statistics.

[0] - https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...

[1] - https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algeb...

[2] - http://codingthematrix.com/

[3] - https://courses.csail.mit.edu/6.042/spring18/mcs.pdf

[4] - https://www.khanacademy.org/math/statistics-probability

[5] - https://onlinecourses.science.psu.edu/stat414/

[6] - https://www.youtube.com/user/joshstarmer/videos

[7] - https://www.deeplearningbook.org

[8] - https://mml-book.com

These are great insights! Thanks so much. Do you think it's worth going through pre-calc/calc deeply? I assumed I should do that first, but it would take quite a while (I haven't taken calc in ~8 years and barely remember more than the basics).

Essence of calculus[0] by 3blue1brown for the basics and the second course in the Coursera Mathematics for Machine Learning would let you get started. You would rarely need calculus more advanced than that covered in the above, and if need be you will be in a position to look it up quickly. If you can sustain your interest in ML over a long period of time and are in no hurry, I would recommend going through all the math mentioned. If you are a top-down learner, the fast.ai course on ML and deep learning for coders will get you started head-first. All the best!

[0] - https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53...

I have. It’s great because it’s developed using geometric intuition (a la 3b1b). Just missing probability/stats.

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