But you do need to know abstract math to build stuff, particularly if what you want to build is deep learning stuff, both models and implementations.
For example, how do you expect to understand how to minimize an utility function if you have no idea of what a gradient is, how you calculate it, and why you want to descend through it.
The course teaches all those things - as the comment you're replying to states, you go deeper and deeper during the course to understand all the details.
There's been a lot of research into teaching strategies that shows that this is often a more effective approach for many people than the bottom up approach widely used in math and CS. It doesn't mean that you learn any less of the foundations - just that it's in a different order.
I seriously doubt that anyone can effectively learn linear algebra, multivariate calculus, optimization and regression models from an onlone tutorial on deep learning. These are subjects whose basics alone take multiple semester-long courses. If a bottom-down approach was remotely effective, no one would bother teaching the basics.
This is a purist approach.
Sometimes, you sacrifice the details to broaden the audience. This has the result of getting more people interested.