posted 3 years ago
There's really very little mathematics. Any level of mathematical preparation is probably sufficient.
Chapter 5 has a section on implementing model learning algorithms that goes through an implementation of naive bayes. That's about as complex as things get, and it's really just a bit of multiplication and division. It's also a totally optional deep dive for readers interested in understanding how model learning algorithms work. It could easily be skipped with little impact on your reading experience. All other model learning algorithms used are either library implementations or simple dummy/stub implementations.
Beyond that, Chapter 6 has a section on model metrics that involves some basic multiplication and division. I think this stuff is pretty important, but I stick solidly to some core fundamentals. I've taught this material to a wide range of software developers face-to-face, and no one has ever struggled to get the core concepts down, with a bit of coaching.
