| 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 |