[siddboots]

If you want to keep up with the maths, the main prerequisites for more rigorous ML is typically undergrad-level calculus, linear algebra, and bit of basic probability and statistics.

For calculus, google "MIT 18.01", "MIT 18.02", (and "MIT 18.03" if you like), which are all freely available on youtube. You should be comfortable with single-variable calculus, and at least familiar with multi-variable techniques.

For linear algebra, try "MIT 18.06", which is Gilbert Strang's MIT course. Or try 3blue1brown's "The essence of linear algebra" series, which is the best explanation I've ever seen of many concepts, but it is shorter and less in-depth than a full course.

For basic statistics, try Khan Academy's "AP Statistics" sequence.

[colmvp]

While I think 3b1b video's are great for developing a better understanding of the purpose of certain concepts, I don't think there's any way around ignore manually doing the problems with say Strangs Linear Algebra book since that's where you spend tim trying to apply these concepts to problems.

[siddboots]

Agreed. Nothing beats practice when it comes to math. I know that I spend far too much time watching course videos, and no where near enough working through problem sets.

[id_rsa]

Working through some of Strang's problems has also helped me. 3blue1brown is a great introduction to give you intuition, but you cannot commit the skills to long-term memory without struggling through problems.

[Jasonseah]

Thx! will look into it!