Some! I did some traditional AI work in school, and recently it’s been a bit of a side project for me at home + work to get a basic handle on Machine Learning: at least enough to build and apply some simple models.
If that’s the side you’re looking to get into, I’d say take a look at Andrew Ng’s courses from Stanford. They are all up on YouTube, and some former TA’s put together a really good cheat sheet that puts all of the core concepts in a single, easy to navigate site:
Prerequisites are any Introductory Linear Algebra, enough calculus to understand how to apply the Chain Rule, and ideally, some programming skill in python or any C-like language.
Videos (Start here at 31 mins https://www.youtube.com/watch?v=UzxYlbK2c7E&t=1890s )
The Stanford lectures are really good at opening up the engine and showing you how machine learning really works and how to tinker with it at a basic level.
I also used a few of the resources I found in this great Hacker News thread: https://news.ycombinator.com/item?id=18996481
Two (from that thread) that were super handy as a supplement (for me) were: https://www.fast.ai/ and https://developers.google.com/machine-learning/crash-course/ because I’ve been working on software long enough that it really helps me to think in code.
It’s been a while since I did my more traditional AI work, so I don’t have internet resources to hand if you’re looking for things like decision-trees and (pruning) and other pre-deep learning crazy techniques.
Let me know if this helps!