Data mining: practical machine learning tools and techniques
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 acclaime...
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, MA, United States
Morgan Kaufmann
[2017]
|
| Ausgabe: | Fourth edition |
| Schriftenreihe: | Morgan Kaufmann Series in Data Management Systems
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780128043578/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes bibliographical references (pages 573-600) and index. - Online resource; title from PDF title page (ProQuest Ebook Central, viewed 02 March 2022) |
| Umfang: | 1 Online-Ressource (xxxii, 621 Seiten) illustrations |
| ISBN: | 9780128043578 0128043571 |
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| 520 | |a 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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| id | ZDB-30-ORH-047408065 |
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| indexdate | 2025-01-17T11:21:52Z |
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| language | English |
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| spelling | Witten, I. H. VerfasserIn aut Data mining practical machine learning tools and techniques Ian H. Witten, University of Waikato, Hamilton, New Zealand ; Eibe Frank, University of Waikato, Hamilton, New Zealand ; Mark A. Hall, University of Waikato, Hamilton, New Zealand ; Christopher J. Pal, Polytechnique Montréal, Montreal, Quebec, Canada Fourth edition Cambridge, MA, United States Morgan Kaufmann [2017] ©2017 1 Online-Ressource (xxxii, 621 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Morgan Kaufmann Series in Data Management Systems Includes bibliographical references (pages 573-600) and index. - Online resource; title from PDF title page (ProQuest Ebook Central, viewed 02 March 2022) 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. Data mining Data Mining Exploration de données (Informatique) COMPUTERS ; General Graphical & digital media applications Machine learning Library, archive & information management Information technology: general issues Databases Enterprise software Data capture & analysis Artificial intelligence Business & Management Computers and IT MIT 601 Frank, Eibe VerfasserIn aut Hall, Mark A. VerfasserIn aut Pal, Christopher J. VerfasserIn aut 9780128042915 Erscheint auch als Druck-Ausgabe 9780128042915 |
| spellingShingle | Witten, I. H. Frank, Eibe Hall, Mark A. Pal, Christopher J. Data mining practical machine learning tools and techniques Data mining Data Mining Exploration de données (Informatique) COMPUTERS ; General Graphical & digital media applications Machine learning Library, archive & information management Information technology: general issues Databases Enterprise software Data capture & analysis Artificial intelligence Business & Management Computers and IT MIT 601 |
| title | Data mining practical machine learning tools and techniques |
| title_auth | Data mining practical machine learning tools and techniques |
| title_exact_search | Data mining practical machine learning tools and techniques |
| title_full | Data mining practical machine learning tools and techniques Ian H. Witten, University of Waikato, Hamilton, New Zealand ; Eibe Frank, University of Waikato, Hamilton, New Zealand ; Mark A. Hall, University of Waikato, Hamilton, New Zealand ; Christopher J. Pal, Polytechnique Montréal, Montreal, Quebec, Canada |
| title_fullStr | Data mining practical machine learning tools and techniques Ian H. Witten, University of Waikato, Hamilton, New Zealand ; Eibe Frank, University of Waikato, Hamilton, New Zealand ; Mark A. Hall, University of Waikato, Hamilton, New Zealand ; Christopher J. Pal, Polytechnique Montréal, Montreal, Quebec, Canada |
| title_full_unstemmed | Data mining practical machine learning tools and techniques Ian H. Witten, University of Waikato, Hamilton, New Zealand ; Eibe Frank, University of Waikato, Hamilton, New Zealand ; Mark A. Hall, University of Waikato, Hamilton, New Zealand ; Christopher J. Pal, Polytechnique Montréal, Montreal, Quebec, Canada |
| title_short | Data mining |
| title_sort | data mining practical machine learning tools and techniques |
| title_sub | practical machine learning tools and techniques |
| topic | Data mining Data Mining Exploration de données (Informatique) COMPUTERS ; General Graphical & digital media applications Machine learning Library, archive & information management Information technology: general issues Databases Enterprise software Data capture & analysis Artificial intelligence Business & Management Computers and IT MIT 601 |
| topic_facet | Data mining Data Mining Exploration de données (Informatique) COMPUTERS ; General Graphical & digital media applications Machine learning Library, archive & information management Information technology: general issues Databases Enterprise software Data capture & analysis Artificial intelligence Business & Management Computers and IT MIT 601 |
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