Rating: 
Summary: Excellent book for the niche audience
Review: If you are interested in the topic of machine learning, this is the start book for you. It is quite informative and covers the actual methods of learning and the concepts involved. It is not the most readable but the author does a good job livening it up. For that matter, I do not know how to convey the topic in a readable fashion so compared against other similar books, the presentation is outstanding. For those in the field, this is a must.
Rating: 
Summary: An excellent textbook for machine learning
Review: In fall 2000, I taught a master's level course in ML to about 25 students at New York University. Fortunately both for me and my students, I was able to use and assign excellent recent textbooks in the area: "Machine Learning" by Tom Mitchell and "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations" by Ian H. Witten and Eibe Frank. I recommend both books enthusiastically. A student who has mastered Mitchell has a solid grasp of the basic element of nearly every method of machine learning currently in use, and of almost every aspect of ML research. A student who has mastered Witten/Frank has a deep knowledge of the major ML techniques, and a strong sense of the opportunities and pitfalls to be encounted when these techniques are put into practice....
Rating:
Summary: Clear, lucid, rigorous,great coverage
Review: It is very rare to find a text that both does rigorous justice to a subject, and also is an enjoyable read. This book is such a rarity
Rating: 
Summary: Now venerable, in the bad sense
Review: It's all right, it covers theory and gets you started in many techniques but - maybe the field was too young when written - I find it unsatisfyingly un-synthesized, unconnected, and short of detail. The 2nd edition of Russell and Norvig is a better introduction, where it covers the same topic which it does for most. I haven't compared the depth of coverage but, at least casually, I don't see anything that this covers that the 2nd ed. Russell and Norvig does not, including the fundamental theory of machine learning. This also sorely needs updating; the field has moved fast and this was written in 1997; a comparison with Mitchell's current course (materials generously available online) shows that about 1/4 of the topics, naturally the most recent, aren't covered by the book, Support Vector Machines, Hidden Markov Models and Boosting to name the best-known. It does not cover statistical methods at all, so if either data mining or the latest techniques are what you're after you'll have to supplement with other books. On its own merits the book covers the central theory and many important topics and doesn't do it badly. It's possible though that it's been superceded by R&N 2nd ed.
Rating:
Summary: Venerable, in both senses
Review: It's pretty well done, it covers theory and core areas but - maybe it was more the state of the field when it was written - I found it unsatisfyingly un-synthesized, unconnected, and short of detail (but this is subjective). I found the 2nd edition of Russell and Norvig to be a better introduction where it covers the same topic, which it does for everything I can think of, except VC dimension. The book sorely needs an update, it was written in 1997 and the field has moved fast. A comparison with Mitchell's current course (materials generously available online) shows that about 1/4 of the topics taught have arisen since the book was published; Boosting, Support Vector Machines and Hidden Markov Models to name the best-known. The book also does not cover statistical or data mining methods. Despite the subjective complaint about lack of depth it does give the theoretical roots and many fundamental techniques decently and readably. For many purposes though it may have been superceded by R&N 2nd ed.
Rating:
Summary: excellent book...must to have one!
Review: just the right content and texr easy to read!
Rating:
Summary: Jack of all trades.
Review: Mitchell provides a good coverage of ML subject matter but niether goes right in-depth, nor gives a digestable overview. I'm a post grad student working in the area, and expect to find one of two things:
1. a clear & concise overview that can bring me up to speed on a body of research - what the competing theories/methods are, when they would be useful, a review of business cases or experimental results justifying various academic positions.
OR
2. In depth analysis of the theory and methods of implementation. I never felt as if I got either from ML.
Rating:
Summary: Buy It!
Review: One of the best books on the subject. Mitchell gives a good introductory coverage to all aspects of Machine Learning. This is not a book full of mathematics, it is a book that gets across ideas and concepts.
Rating:
Summary: A great introduction to the field of machine learning!
Review: This book does an incredible job of presenting sophisticated material in a clear and easy to understand style. I highly recommend it to anyone interested in the field. Absolutely first rate!
Rating:
Summary: So - so
Review: This book is a good introduction to the field, but I think the notation can be quite cumbersome at times. I've seen the concepts presented elsewhere in less confusing form, but it's a good general source as it includes a considerable amount of information from relatively current research. The examples are typically very easy to understand, though they aren't always complicated enough to make the notation easy to understand.
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