This is fantastic, I will share it with students. A very minor comment:

>So, there’s a total of three types of vectors in NumPy: 1D arrays, 2D row vectors, and 2D column vectors.

I think you can have 'vectors' of arbitrarily high dimension, by adding empty axes, or doing, say, .reshape(-1, 1, 1, 1).

I would also tie this point with the rule for broadcasting that dimensions which do not match need to have a value of one. I'm endlessly confused about broadcasting, and find the explicit rule useful.