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Data mining : practical machine learning tools and techniques / Ian H. Witten...[et al.].

Contributor(s): Witten, I. H | Frank, Eibe | Hall, Mark A | Pal, Christopher JMaterial type: TextTextLanguage: English Publication details: Cambridge, MA : Morgan Kaufmann, 2017. Edition: 4th edDescription: xxxii, 621 p. : ill. ; 24 cmISBN: 9780128042915; 9780128043578Subject(s): Artificial intelligence | Data miningDDC classification: 006.3 Online resources: WorldCat Details
Contents:
Table of contents Part I: Introduction to data mining 1. What’s it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what’s been learned Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond
Summary: 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 https://www.cs.waikato.ac.nz/~ml/weka/book.html
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section 		Non-fiction 006.3 DAT 2017 (Browse shelf(Opens below)) C-1 Not For Loan 31653
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section 		Non-fiction 006.3 DAT 2017 (Browse shelf(Opens below)) C-2 Available 31654
Total holds: 0

Includes bibliographical references (pages 573-601) and index.

Table of contents Part I: Introduction to data mining 1. What’s it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what’s been learned Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond

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 https://www.cs.waikato.ac.nz/~ml/weka/book.html

Computer Science & Engineering Computer Science & Engineering

Sagar Shahanawaz

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