Data mining practical machine learning tools and techniques 3rd pdf
Data Mining: Practical Machine Learning Tools and TechniquesWitten , E. Frank , and M. BibSonomy The blue social bookmark and publication sharing system. Toggle navigation Toggle navigation. Log in with your username.
Data Mining Practical Machine Learning Tools and Techniques, Third Edition Morgan Kaufmann Series in
Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.)
Part I. Machine Learning Tools and Techniques: 1. What's iIt all about? Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5.
Data mining: practical machine learning tools and techniques.—3rd ed. /. Ian H. Witten, Frank Eibe, Mark A. Hall. p. cm.—(The Morgan Kaufmann series in data.
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Machine learning provides an exciting set of technologies that includes practical tools for analyzing data and making predictions but also powers the latest advances in artificial intelligence. We have written a book that provides a highly accessible introduction to the area but also caters for readers who want to delve into the more mathematical techniques available in modern probabilistic modeling and deep learning approaches. Chris Pal has joined Ian Witten , Eibe Frank , and Mark Hall for the fourth edition, and his expertise in probabilistic models and deep learning has greatly extended the book's coverage. To make room for the new material, we now provide an online appendix on the Weka software. It is an extended version of a brief description of Weka included as an appendix in the book.
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition , 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. It is one of the best of its kind.