Machine Learning (McGraw-Hill International Editions Computer Science Series)

I've bought quite a few books on machine learning over the years, but Mitchell's stands out. It's not just the depth of the content but the way it's presented. I found it easy to follow and surprisingly engaging. Really glad I added this to my collection. Would recommend to anyone diving into ML! 5 stars for sure. 🌟🌟🌟🌟🌟

I studied this book during my Ph.D. in Computer Science.Tom Mitchel provides a very concise approach to machine learning but exploring various models and techniques and exposes you to what Learning is ; machine learning is described in terms of mathematics .I love how various examples in the book describe things in solid Math and statistical information.Great Chapters on Decision tree, Bayesian Learning, etc,...It is just a joy to be able to find one of my favorite books and be able to read it againThe Material is definitely timeless and applies well to current State of the art in Machine Learning as well.Highly recommend it if you can find a copy, you should have one in your home if you are interested in this subject.The version I received was free of any marks , looked fresh and although I had a hard copy in the past (that I had lost ) and this one is soft cover, paper is good quality .Delivery was also good and on time.

This book is really good for an introduction to all types of machine learning algorithms. It has good detail for most of the algorithms.Some of the other reviewers say that it lacks depth. This is somewhat true. There are a few chapters that leave you wanting more, but overall I still think this is a good book.I have several machine learning books, and most of them are more in depth, but lacking a broader overview of machine learning. So if you want an overview of different problem solving techniques, this is the book for you. It has enough theory to keep most people happy. If you want to know the core motivational aspect to the finest details, this book will be lacking in some areas. Other books may have more detail, but just know they won't cover as much of the overall subject.This is always my go to book for trying to remember something. It's small, light, and enough to get me back on track. I have other books for more in depth reading, but they don't cover as much of the subject of machine learning as this one.

Exceeded expectations. For a 20 year old book an a fast-moving field, this book is right-on. This is not a practical guide, as many of the tools that are used now did not exist when this book was published, but provides an excellent theoretical grounding in how many popular machine learning models are constructed.

This is a well written and laid out book. It was a great resource in my graduate level AI class. If there was anything I didn't quite understand during lecture I was able to look up the material in the book and get my bearings. The assignments in the book were also very helpful in learning the material. Mitchell knows his stuff and is still considered one of the brightest in the field.

This book is old and out of date.It does not cover lots of modern algorithms and techniques.A boring and tedious read.

GREAT QUALITY FOR PRICE! Although this seller had few reviews, I was somewhat doubtful, but the item I received shipped in about 2 days with great quality for a used book! Love it!

Needed it for two of my classes. Great book for classes that use it as a textbook. Feels a bit overpriced.

Temas excelente

Best introductory book I have read on the topic. Emphasis is more on explaining what the algorithms do as opposed to providing many recipes with no real insight.

Book was delivered as promised . Good condition , nice packaging. Now as far as the contents of the book go , I've understood that the book serves as a basic guide to understanding machine learning . If you need an advanced understanding , then I probably would not recommend this one.

C'est le livre qu'il faut avoir lu sur le sujet. C'est theorique et trappu, mais on est venus pour ca !

This book is old, some techniques widely used nowadays, like support vector machines, are not covered.Anyway, what's there is explained in an easy-to-understand way, and their basic concepts are still valid.Concept first, notation follows. Important notions are recalled as needed, so you can read any chapter without necessarily having read all the others.All in all it is a nice introduction.