TL;DR The book should have been named "Building Machine Learning Apps in Java for Dummies". Worth reading if such name excites you.
This book aims to become a comprehensive guide in commonly used Machine Learning technics. And generally it succeeds in that with one reservation: it more resembles a sophomore synopsis, than a professor's lecture.
I should say, that I couldn't manage to finish the book, and had read only Chapters 1,2 and 5. But the structure of each chapter is roughly the same: a brief high-level description of the common approaches in the described sphere. Then a list of Java libraries, available for the task. Selection of one of them and a description of how solid, robust and mature it is. Then several pages of Java code, showing how to prepare input parameters and actually make use of the libraries. Finally a note, that there are some limitations or pecularities, but discussing them in more detail is out of the scope of the book. Thus, the amount of new material for anyone at least slightly familiar with the subject and programming in general is minimal (I would say, that for me it was around 10%.) No insights, nothing novel, that can't be found on the first page of Google results on the topic, no account of personal experience or "war stories". Such book doesn't require 20 years of industrial experience under the hood to write — this could be done by any working programmer. The recipe seems simple: for each topic take the first two pages of Wikipedia entry on it, list the available libraries for solving the problem, add a couple of examples from their manual, bingo!
There's also another problem: the code is in Java. This is good for the authors, as it increases the book size and makes it seem much more solid, than in reality. But all the unnecessary cruft like half-line type declarations and bookkeeping code for loops (which amounts to more than half of the code, I'd say) makes it really hard to follow along. You need to really focus to be able to translate the code in mind to some high-level conceptual form before grasping it. (Although, there's a shortcut: just assume, that all the code is mostly irrelevant and skip). It's like the authors used the trick of all students: hide your poor understanding of the subject behind all the recondite words you've heard. So the advice for any algorithmical book: if you want to make the code easy to follow write it in Python or Lisp. At least it would make really apparent, if there's not a lot of essence to it... ;)
So the book is worth reading only if you're new to the field, don't want to spend time learning the subject, but need to get some working program fast (and don't object using Java for that).
