I think that any maturing field needs a hierarchy of explanations that go progressively deeper into the subject - the explanations of the second law of thermodynamics that you get in freshman chemistry course are superficial compared to what you would deal with in a graduate course, but this doesn't mean that chem101 should be taught at the grad level, or that it is pointless to teach freshmen about the second law. The real danger is if you lie or misrepresent the facts in an attempt to simplify. I'm sure there are some errors and falsehoods in PCI but I didn't see anything too bad in the one day that I've had the book.
Learning API's by reading the docs sounds like trying to learn english by reading the dictionary - It's just my personal preference, but I feel that I learn more effectively by reading real source code that works (even if it is horribly written)
To each his own - I thought this book could easly be condensed into a list of mathematical theorems - a page worth of links to wikipedia.
1) The examples are real python code
Really crappy python code... definitely not the kind of code one should be looked at as example.
He doesn't just test algorithms on standard datasets like MNIST or MovieLens - He gives programming examples for how to extract your own datasets e.g. by showing how to use the eBay api to download prices from eBay, or how to use the facebook api.
He combines math theory with code and API's? What's the point of that?
If you want to learn how to code python, read a python book. If you want to learn math, read a math book. If you want to learn API's, read the API docs. If you want to learn all three, read all three, but don't think that you know any of them if you see a bunch of badly coded examples of the results of knowing them.