This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining--including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.
Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.
Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books. Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly...
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This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving...
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I'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation. This book is so...
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The first edition of this book was good, but this is a huge improvement. The writing is really great, very clear, even when it heads into deeper waters. The explanation, for instance, of the various algorithms for accomplishing attribute discretization is very clear, even as the equations start to get very long and complicated. It's pretty incredible that this book is so readable, kudos to the authors for that. Most importantly,...
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This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This...
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