#180 overall
Computational Linear Algebra
fast.ai
3 recommenders · 3 threads · recommended in 3 years, 2019–2022 · score 2.4
A free online course from fast.ai covering computational linear algebra for programmers, focusing on numerical methods such as matrix decompositions (including SVD) and how they are implemented in code.
Commenters recommend it as a practical follow-on to 3Blue1Brown for learning how linear algebra is actually computed, aimed at people who already know matrix and vector basics.
Mathematicsintermediatefreevideo lectures#linear algebra#numerical methods#svd#matrix decomposition#fast.ai
Recommendations by year
20062026
If you want to get better at geometry a very good start is brilliant.org. They have five great geometry course that's good for learning concepts visually and has some basic exercises, but not enough for drilling.
When it comes to exercises, brilliant.org is lacking in volume. Khan Academy is a great supplement for geometry, single and multivariable calculus although brilliant.org goes a bit further than Khan Academy with Linear Algebra, Group Theory and more.
Khan Academy also has a linear algebra course, but I found it to be kinda crap with no exercises. For linear algebra it's better go with brilliant and 3blue1brown's linear algebra videos, then a good continuation would be Linear algebra done right by Sheldon Axler and also fast.ai's free online course Computational Linear Algebra for Coders.
I would heavily second this. I would also recommend, for those who are interested in computational linear algebra, going through fast.ai's course when you finish 3blue1brown: https://github.com/fastai/numerical-linear-algebra/blob/mast...
Yes for linear algebra, though it's probably best suited to people with at least some exposure to matrix/vector math (for example you've used SVD but have no idea how or why it works): https://www.fast.ai/2017/07/17/num-lin-alg/
For probability, machine learning is more about statistics (the two are related, but courses explicitly about probability will cover different things), so I would lean towards that. An Introduction to Statistical Learning in R (ISLR) is a frequently recommended book. You can ignore the R and do the exercises in Python.
If you actually want to learn about probability, you can look at MIT's course: https://ocw.mit.edu/resources/res-6-012-introduction-to-prob...
EDIT: If you've never been exposed to calculus, many people swear by Khan Academy's videos.