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Data Mining: Practical Machine Learning Tools and Techniques(2016) (.PDF)

Author: INDEXER on 5-05-2017, 14:04

Data Mining: Practical Machine Learning Tools and Techniques(2016) (.PDF)


Data Mining:Practical Machine Learning Tools and Techniques(2016) - PDF
Morgan Kaufmann; 4 edition (December 1, 2016) - ISBN-13: 978-0128042915 - 654 pages - PDF - English
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. The book continues to provide references to Weka implementations of algorithms that it describes. The Weka MOOCs provide activities similar to the tutorial exercises in the 3rd edition. We now also provide information on other software: the computational ecosystem for machine learning has grown enormously since we have written the third edition in 2011. A table of contents for the fourth edition, indicating where we have added new material, can be found further down this page.

"If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start."

-Jim Gray, Microsoft Research

"The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject."

-Dorian Pyle, Director of Modeling at Numetrics

"This book would be a strong contender for a technical data mining course. It is one of the best of its kind."

-Herb Edelstein, Principal, Data Mining Consultant, Two Crows Consulting

"It is certainly one of my favourite data mining books in my library."

-Tom Breur, Principal, XLNT Consulting, Tiburg, Netherlands
About the Author

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>

Mark A. Hall holds a bachelor's degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.




[Ian_H._Witten]_Data_Mining_Practical_Machine_Lear.pdf -  4.8 MB 


Tags: Data, Mining, Practical, Machine, Learning, Tools, Techniques

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