Data Mining: Practical Machine Learning Tools and Techniques

Description

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Review: Four Stars - The front page is featuring wrongly as geophysics. Review: Continues to lead introductions to data mining - I've read and reviewed the 1st, 2nd and now the 4th edition. The authors are genuine experts, at the front of their fields, and by adding new contributors have been able to both update existing topics as well as add authoritative treatments of new ones. I recommend this text to anyone seeking a serious introduction to data mining. The emphasis is practical rather than theoretical, but there are pointers to the theoretical literature for those wanting them. The practical emphasis serves those wanting such, and provides motivation and context for the approach. For those with the necessary mathematical, statistical and computing background there are certainly a plethora of more advanced treatments, but Witten et.al. may well be the best available introduction to the subject for almost everyone.