Andrew Ng's course definitely requires some linear algebra knowledge, and explains how things work under the hood.
Same goes for fast.ai
I think even that level of knowledge is not always necessary. Often, just having the intuitive understanding can be enough to get great results e.g: understand that this is a black box that takes features in, predicts things, and you have to help it by giving good features; or understand that word2vec builds vector representations based on words that co-occur in the same context.
Speaking of learning/reviewing linear algebra, I wrote the NO BULLSHIT guide to LINEAR ALGEBRA which covers all the material from first year in a very concise manner.
Nice! Did you work though some of the exercises and problems? Don't be a tourist in the land of math!
If you don't want to bother with pen-and-paper (which is the best, but takes very long), you should at least try some problems using the computer-assisted approach. See samples solutions to first few chapters: https://github.com/minireference/noBSLAnotebooks
>Andrew Ng's course definitely requires some linear algebra knowledge,
Extremely basic. Anyone with an undergrad engineering degree will have covered what's needed for the course. His course was almost patronizing, calling people who knew the basics of calculus as "advanced students".
I got halfway through his course and it seemed to do a decent enough job of teaching it as you go. the main thing I struggled with was the mathematical notation.
I think even that level of knowledge is not always necessary. Often, just having the intuitive understanding can be enough to get great results e.g: understand that this is a black box that takes features in, predicts things, and you have to help it by giving good features; or understand that word2vec builds vector representations based on words that co-occur in the same context.