Most ML problems in real life don’t constrain you to use linear regression or a CNN either. But there will be some metric you need to optimize.

What would take this repo to the next level is to have a reproducible data generation function for each exercise as well as a reasonable metric which must be passed. I don’t see anything that requires my classification auc to be over 0.5 which would be a basic criteria of bug-free code.

It's also what most people ask when they go for interviews.

I was reverse engineering the ML interview pipeline for myself and that's how I stumbled upon all this.

I think the data aspect does make sense tho. I might add that as the next thing to do