I feel like I saw one once but lost it -

Is there a githubrepo/tutorial for how linear algebra is used for a very small model just to demonstrate how that allows it to "learn"?

I've got the calc, I just don't understand what the matrix multiplication "does"

Watch Karpathy's recent lectures. They're gold. Start here[1] with micrograd[2]. It doesn't use linear algebra/matrices to start, but the principles are the same. The matrix multiplication is how the weights of the connections between neurons and the input values are combined (to form an activation value that then may lead to that neuron "firing" or not, depending on whether it passes some threshold function). We use matrices to model the connections between neurons - each row is a connection, and each column is a weight corresponding to an input.

[1] https://www.youtube.com/watch?v=VMj-3S1tku0 [2] https://github.com/karpathy/micrograd

I cannot recommend Andrew Ng's courses on Machine Learning enough. Something like this seems like it would cover everything you're looking for.

https://www.coursera.org/learn/machine-learning

I cannot speak to the author of the content of this github repo, but it appears they have completed the course and included all of the solutions here. It might let you jump right to what you're looking for.

https://github.com/greyhatguy007/Machine-Learning-Specializa...

What math pre-reqs does it need for someone who never made it to college level maths?

Based on my experience as long as you have a good foundation in the basics of algebra you’ll be able to pick up the rest from the course.