yeah. I tried “accelerate” and “exaggerate” before “cause”, but it got confused and repeated the prompt as a caption meme on random images of forests

“Introduction to Algorithms” by Ronald Rivest is the standard college level textbook for this topic. I highly suggest starting there. For my undergraduate degree, this was meant to be taught in the first half of the second year and there’s no specific mathematical requirements other than understanding advanced notation.

“Cracking the coding interview” will likely expose you to questions you’ll see again during the interview process, but it lacks the formality.and structure of a mathematics text.

I would ignore the people who say you should deploy a model from someone else as that will teach you next to nothing about how this stuff works.

I would start with an older model and framework (e.g. scikitlearn) and go through all the processing, prediction, and evaluation steps using a model that’s fairly simple to understand. Since you already know about linear regression, start with some of these linear models.

Then, and only then, would I worry about neural networks and deep learning, since the main difference is a non-linear activation function and a much more complicated set of weights (model parameters in the linear regression language).

Here is an example

Source: PhD in neural networks