Practical Machine Learning Tools And Techniques Pdf
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- Data Mining: Practical Machine Learning Tools and Techniques
- Data Mining Practical Machine Learning Tools and Techniques 3rd Edition
- Data mining - practical machine learning tools and techniques, Second Edition
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Witten , 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
Smith and Eibe Frank. Springer, New York, NY, It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information. The WEKA workbench. Morgan Kaufmann, Burlington, MA, 4 edition,
Search this site. Book by Pierre Labbe. Book by Fabrice Lemainque. EPUb by Pirmin Lemberger. Building Scalable Apps with Redis and Node. CMMI 1. Gimp 2.
Data Mining Practical Machine Learning Tools and Techniques 3rd Edition
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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. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. It containsPowerpoint slides for Chapters This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Author: Ian H.
Data mining - practical machine learning tools and techniques, Second Edition
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