#195 overall
Linear Algebra
MIT OpenCourseWare
2 recommenders · 2 threads · recommended in 2 years, 2008–2022 · score 2.3
An undergraduate linear algebra course from MIT OpenCourseWare, taught by Gilbert Strang, delivered as video lectures covering matrices, vector spaces, and related topics.
Commenters call Gilbert Strang's linear algebra lectures the best and use MIT's videos to relearn college math.
Recommendations by year
20062026
computer science is an extremely broad area. Narrow it down, maybe?
1. Introduction to Computational Thinking at MIT was a great one.
2. Robert Ghrist has the best set of SV Calculus courses on Coursera.
3. Gilbert Strang's LA courses are the best.
4. For Algorithms course, I would recommend Sedgewick and Wayne's course on Coursera.
5. For PL course, go with the WashU course on Coursera.
6. For Machine Learning, you should start with Andrew Ng's Machine Learning.
7. Cryptography I, II by Dan Boneh is a great one.
8. Introduction to Programming with Python at MIT on edX is a good course. And so is CS50 by David J. Malan.
9. The nand2tetris course on Coursera.
These are some that I can vouch for. You did not mention your level or what topic you want to learn.
The best way is to do a google search for moocs, and try several of them out, and pick one with the teacher whose teaching style you like the most.
In MOOCs that are not mandated, the personality of the teacher matters a lot. In my opinion- it is the most important factor.
You should also look up courses where all materials are publicly available- tests, assignments, homework problems, etc. If some parts are not, you can always email a prof, and they will almost never turn you down.
Follow discussions on Reddit. Engage in discussions, too.
You should look up https://teachyourselfcs.com and check out the recorded courses there. Do the ones that you would like.
First of all, kudos on the turnaround. I think the most important thing to keep in mind is except for a few scattered geniuses, no one is inherently good at math. I know it sounds like a tired metaphor, but math really is a different language (which takes a lot of study and practice to achieve fluency).
If you lack fundamentals, skip the sources and look for the people. Find a good teacher and take their class. This will probably entail an intense week of course shopping at a local school. Friendly fellow students can be a huge boon, at least for me, since I solidify concepts best in conversations with peers.
If you, for some reason, absolutely cannot take a class in person, I would encourage you to find a study partner and watch online lectures together. I really like MIT's Linear Algebra and Differential Equations videos (http://ocw.mit.edu/OcwWeb/web/courses/av/index.htm#Mathemati...), but but I don't know if those are at your level.
Machine Learning.
In 2018 I went from "zero to sixty", so to speak. At the beginning of the year I knew nothing about ML, and my math skills had gotten so rusty I couldn't even remember how to do simple derivatives or multiply two matrices.
In the first half of 2018 I relearned the college math that I had forgotten by working through MIT OCW's Linear Algebra, Calculus, and Probability courses.
In the Fall, I enrolled in Carnegie Mellon's 10-601 course, graduate "Introduction to Machine Learning". It was hard, it was a ton of work, but I did well and it was completely worth it as I now have a solid understanding of the basics of the field and can continue studying this upcoming year and into the future.