Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition
- 13h 23m
- Christopher J. Pal, Eibe Frank, Ian H. Witten, Mark A. Hall
- Elsevier Science and Technology Books, Inc.
- 2017
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.
Key Features
- Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
- Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
- Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
- Includes open-access online courses that introduce practical applications of the material in the book
Readership
Data analysts, data scientists, data architects. Business analysts, computer science students taking courses in data mining and machine learning
In this Book
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What's it All about?
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Input—Concepts, Instances, Attributes
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Output—Knowledge Representation
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Algorithms—The Basic Methods
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Credibility—Evaluating What's Been Learned
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Trees and Rules
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Extending Instance-Based and Linear Models
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Data Transformations
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Probabilistic Methods
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Deep Learning
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Beyond Supervised and Unsupervised Learning
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Ensemble Learning
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Moving on—Applications and beyond
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References
