The importance of math in understanding AI is kind of overblown. Binary Neural Networks for example are in theory worse, but the savings in computational time makes up for quite a bit.
That said, a wide foundation including a deep understanding of linear algebra is more useful than just covering the specifics relevant to AI.
That makes sense. But for me for instance, who's trying to learn about AI and have a math background, I like courses that explain the math behind everything because it makes it easier to understand the concepts. It's also a good way to check that I understand what's going on. And finally, I find it easier to skim over a text has equations, they easier to parse for me since I'm a slow prose reader. So I can go through a text by skimming through the math, and when I stop understanding, I try to backtrack what's going on and only move on once I understand what was blocking me.
EDIT: And this applies to everything. Ex: I've been playing the guitar for over a decade but have been struggling with music theory. I've recently tried to apply the little algebra I know to it to try and find structure. I've found some really cool articles, for instance [0], and it's helped quite a bit believe it or not !! For French speakers that like music, math, and algorithms, I highly encourage a presentation by Moreno Andreatta [1] where he closes the presentation by performing a "rotation around the Do of the Beatles' Hey Jude".
Recent report from my mother, who's a physicist with a strong math background, no statistics: Take the coursera intro course.
She said that the first hour or two were a little frustrating for the kinds of reasons you mention. After that, she could skip the "long" explanations & examples by going to the math directly. You'll progress 5X faster than normal after that.
She "got it" by creating ML "proofs" for math problems without doing math. She didn't like all the image recognition examples. Said it's a confusing place to start.
You are probably stuck with learning from a source that assumes you don't know math, because most people (including me) don't. This course is probably not for you if you want theory, as it's focused on "using" ML algorithms, not writing them. That said, this might be useful to you if you already have stuff that you want to do, and need a way of doing the "sticks and duct tape."
That said, a wide foundation including a deep understanding of linear algebra is more useful than just covering the specifics relevant to AI.