I wholeheartedly agree with this. As a software developer without a strong math education, any time I open a "from scratch" ml learning resource, I immediately close it because I don't have an understanding of the math happening in the black box of numpy.
Can you elaborate? As an experienced numpy user, I don't really have an intuitive feel for what you describe. Numpy isn't really math it's mostly an array library for splitting, merging, reshaping arrays, swapping dimensions etc. and applying operators on all elements at once.
Outside of the things under np.linalg or np.fft, I cannot think of much really difficult math in numpy.
To grok numpy what you really need is a good mental model of the multidimensional array, reshapes, reductions, transpose and so on. But this isn't really math, it's more data wrangling and kinda boring and tedious "data chores" we need to do before we get to the interesting parts of the job.
Thank you for the Sentdex recommendation.