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Data Mining : Practical Machine Learning Tools and Techniques

By: Christopher J. Pal (Author) , Eibe Frank (Author) , Ian H. Witten (Author) , Ph.D. Foulds James (Author) , Mark A. Hall (Author)

1 in stock

Ksh 15,050.00

Format: Paperback or Softback

ISBN-10: 0443158886

ISBN-13: 9780443158889

Edition Number: 5

Publisher: Elsevier Science & Technology

Imprint: Morgan Kaufmann Publishers In

Country of Manufacture: GB

Country of Publication: GB

Publication Date: May 6th, 2025

Publication Status: Active

Product extent: 688 Pages

Weight: 1532.00 grams

Dimensions (height x width x thickness): 19.10 x 23.30 x 3.30 cms

Product Classification / Subject(s): Data mining
Machine learning

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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

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


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