Inane verbiage with no educational content. Just constant fawning and endless lists of potential and current applications of learning algorithms. The writing is beyond tiresome. Here's just one paragraph:

"You’ve reached the final stage of your quest. You knock on the door of the Tower of Support Vectors. A menacing-looking guard opens it, and you suddenly realize that you don’t know the password. “Kernel,” you blurt out, trying to keep the panic from your voice. The guard bows and steps aside. Regaining your composure, you step in, mentally kicking yourself for your carelessness. The entire ground floor of the tower is taken up by a lavishly appointed circular chamber, with what seems to be a marble representation of an SVM occupying pride of place at the center. As you walk around it, you notice a door on the far side. It must lead to the central tower—the Tower of the Master Algorithm. The door seems unguarded. You decide to take a shortcut. Slipping through the doorway, you walk down a short corridor and find yourself in an even larger pentagonal chamber, with a door in each wall. In the center, a spiral staircase rises as high as the eye can see. You hear voices above and duck into the doorway opposite. This one leads to the Tower of Neural Networks. Once again you’re in a circular chamber, this one with a sculpture of a multilayer perceptron as the centerpiece. Its parts are different from the SVM’s, but their arrangement is remarkably similar. Suddenly you see it: an SVM is just a multilayer perceptron with a hidden layer composed of kernels instead of S curves and an output that’s a linear combination instead of another S curve."

Had enough yet? There are chapters full of this drivel. This is actually representative even of parts of the book that aren't an acid trip. Just like the rest of the book this passage mentions concepts without explaining any of them and mixes everything together without any reason or structure. Who is this book for? There are in-jokes dotted around like the Jennifer Aniston one or concepts like centaurs (in relation to chess) and neither they nor their backstories are ever explained yet it's clearly aimed at the general reader. ( )

I'm am a Machine Learning researcher, and this book gives a good introduction in a human way of how can I present some concepts so the people can understand.

But after all that introduction, it's quite boring. I don't believe in some points that the author gave about The Master Algorithm. ( )

I was looking for a book that explains ML and AI concepts for non-practitioners. It did a great job at that. It goes through the most used 7 types of ML algorithms/concepts and explains how they work using high-level math and analogies. It blends a bit of the history of the field so that's always nice to contextualize the information.

What made me only give it 3 stars are the detours it keeps making into predicting the future and the crazy soft stance it takes towards the tech giants like FB and GOOG. I'm not sure if Mr. Domingos is still willing to be have such a friendly opinion of them after the latest findings on how they use ML and the many privacy breaches they've had, but I hope not. In any case I'm judging the books version of events and it's lacking any criticism towards the uses of ML in manipulating public opinion.

The book is great at teaching you the high-level workings of ML. The problem I had was it kept trying to do more than that by taking a stab at predicting the future of the field influence on humanity. Even for a specialist, it's mostly speculation. ( )

Machine Learning Made Easier (or NOT!): "The Master Algorithm" by Pedro Domingos Published September 22nd 2015.
 
 
How can one become an expert in ML? All one needs is a basic background in (multivariate) Calculus, Linear Algebra, and Probability. ML is math. If one wants to understand the techniques, one has to understand the math. No shortcut. If one wants to start looking into the field of ML, this book is for you. If not, stay well clear.
 
My background is in computer science and software engineering and I've been interested in ML since I can remember. In 2013 I took Andrew NG's ML class at Stanford University (for those of you who want to dive into stuff like this here are mynotes of the class; while learning the needed math can look daunting at first it is actually quite fun once you get into it), and I was never literally the same…After that I made some Python coding to get a feel for the real thing, which I’m still doing to this day.
 
Humans ARE machines, albeit biologically-based. Billions of highly interconnected neurons receiving sensory input, lots of internal feedback, and signals that go out to motors, etc. Emotions, feelings, consciousness, are all just “concepts” we've constructed through a mixture of self-introspection and communicating with other self-introspecting machines (humans).
 
Read on, if learning comes as second nature to you. ( )

This is a great book about machine learning for both: people who want to know more about it and people who are in this business. Domingos is a great writer with the ability to present complex ideas in easily digestible form. Here you will learn not only what you can do today with machine learning, but also the various philosophical camps, their approaches and finally the limitations of machine learning.

What is interesting about machine learning is that all algorithms in use today are statistical methods developed primarily in the 1960s and 70s. The only negative thing I have to say about this book is that I find some of his optimism a bit unfounded given the limitations of all of these techniques. ( )