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- 654pages
- 23 heures de lecture
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This fourth edition offers a comprehensive grounding in machine learning concepts and practical advice for real-world data mining applications. It covers everything from input preparation and output interpretation to evaluating results and the algorithmic methods central to successful data mining. The edition includes extensive updates reflecting recent technical advancements, with new chapters dedicated to probabilistic methods and deep learning. Additionally, it features a new version of the popular WEKA machine learning software from the University of Waikato. The authors, Witten, Frank, Hall, and Pal, integrate contemporary techniques with cutting-edge research methods. A companion website provides PowerPoint slides for Chapters 1-12, serving as a valuable teaching resource. The online appendix focuses on the Weka workbench, offering extensive learning aids for the accompanying open-source software. The table of contents highlights the new sections in this edition, along with reviews of the first edition and errata, ensuring a thorough educational experience for readers.
Achat du livre
Data Mining, Christopher J Pallister, Ian H. Witten, Eibe Frank, Mark A Hall
- Langue
- Année de publication
- 2016
- product-detail.submit-box.info.binding
- (souple)
Modes de paiement
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- Titre
- Data Mining
- Sous-titre
- Practical Machine Learning Tools and Techniques - Fourth Edition
- Langue
- Anglais
- Éditeur
- Morgan Kaufmann
- Publié
- 2016
- Format
- souple
- Pages
- 654
- ISBN10
- 0128042915
- ISBN13
- 9780128042915
- Séries
- Mots clés
- Nonfiction, Technologie & Ingénierie, Manuels et guides, Informatique & Internet, Technologie, Intelligence Artificielle, Base de données, Analyse de données, Apprentissage automatique
- Description
- This fourth edition offers a comprehensive grounding in machine learning concepts and practical advice for real-world data mining applications. It covers everything from input preparation and output interpretation to evaluating results and the algorithmic methods central to successful data mining. The edition includes extensive updates reflecting recent technical advancements, with new chapters dedicated to probabilistic methods and deep learning. Additionally, it features a new version of the popular WEKA machine learning software from the University of Waikato. The authors, Witten, Frank, Hall, and Pal, integrate contemporary techniques with cutting-edge research methods. A companion website provides PowerPoint slides for Chapters 1-12, serving as a valuable teaching resource. The online appendix focuses on the Weka workbench, offering extensive learning aids for the accompanying open-source software. The table of contents highlights the new sections in this edition, along with reviews of the first edition and errata, ensuring a thorough educational experience for readers.