Data Mining Practical Machine Learning Tools and Techniques
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Form:Einzelkauf Download
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Sprache:Englisch
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eBook Format:ePUB 3
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Auflage:5. Auflage
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54,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
04.02.2025
Verlag
Elsevier Science & Techn.Seitenzahl
688 (Printausgabe)
Dateigröße
23614 KB
Auflage
5. Auflage
Sprache
Englisch
EAN
9780443158896
**2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner**Data Mining: Practical Machine Learning Tools and Techniques, Fifth 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 new 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 more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research
- 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
- Features in-depth information on deep learning and probabilistic models
- Covers performance improvement techniques, including input preprocessing and combining output from different methods
- Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software
- Includes all-new exercises for each chapter
- 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
- Features in-depth information on deep learning and probabilistic models
- Covers performance improvement techniques, including input preprocessing and combining output from different methods
- Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software
- Includes all-new exercises for each chapter
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