Oh, I see what you did there.

I like little man's mini-bomber jacket.

From looking at the picture, I'm older than his kids and I'm just in my 40's. 😆

I'd like to buy some punctuation, Alex.

You know people make a lot of mistakes, too, right?

well shit I was less wrong than the other person lol

imminent domain

You do realize that in the era before planes and automobiles there was a well-functioning and world-class passenger rail service that went to every major city in the country right? And that was in the 1800's and early 1900's so we could make it work now if we really wanted to but we are collectively addicted to cars.

Might I suggest reducing your dataset sizes by about an order of magnitude to ~10k or even less? Less than 1000 records would be ideal.

If it is a learning environment, you want a pretty quick turnaround on training models, say less than 30 seconds. Of course, it can take much (much) longer on actual production systems with huge training sets, but it is going to be frustrating for students if they have to wait minutes for their models to train. I'd test this beforehand and make sure that they won't get bogged down by this. (Plenty of small ML datasets out there which are still interesting and instructive.)

The vast majority of machine learning topics can be covered without needing GPU compute, with the exception of neural nets and deep learning. That's like a course unto itself though. If model complexity and dataset sizes are kept reasonable then CPUs should be fine.

I don't know what it costs but we have used a notebook-based platform called CoCalc which I thought was really solid.