Take a look at the primary algorithms used commonly in AI today - nothing exceeds high school level math.

I posted about this earlier today but ML really should be demystified. You can write a lot of commonly used algorithms in 100 lines or fewer. The math is not complex. If you can get past the notation and buzzwords like "deep learning" (it's an artificial neural network, itself a grandiose term) you'll see it's not as daunting as most think.

The reality is most "data scientists" will be working on implementation rather than creation. They'll be working on data sets and error analysis, not creating the next buzzword-laden algorithm.

Most ML algorithms can be treated as an optimization problem.

Convex optimization is not high school level math.

If you've got a lock on calculus, programming, and linear algebra, then you've got the skills to understand deep learning. Most time spent working on DL is not in the megamath part, it is in finding good network structures to optimize results.

I think a lot of resources out there at the moment are full of ML jargon and math. And a lot of new stuff that is coming out attacks the absolute beginner. This kind of sucks .. I started using torch, familiar with old fashioned NN and just wanted to quickly get up to speed on convolutional networks. It has been a PAIN (resources I find are either too deep or not deep enough). In any cases .. thanks for putting your links .. they were most helpful.

You are right that most resources aren't that great at showing you the way. There is a lot of grinding to get past obstacles.

We are trying to address this soon!