Both the intro and this one are great reference books but I don't find them suitable to study as the main textbook. They cover a large number of topics so the depth of each topic is pretty limited. Keep in mind if you are considering to study these.
I find myself wishing for a book that instead of listing techniques with a few shallow examples per technique , would instead focus on a meaty problem and then apply different techniques to it iteratively showing how a practitioner would derive value. Is anyone aware of such books ?
Judging from the table of contents, he probably could have dropped the "probabilistic" from the title and just called it "machine learning".