practical machine learning tools and techniques Witten, Ian H. creator author. Frank, Eibe author. Hall, Mark A. author. Pal, Christopher J. author. Foulds, James R. author. text 50 Hampshire Street, 5th floor, Cambridge, MA, 02139 United States Elsevier Inc. c2026 Fifth edition. monographic eng

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xl, 760 pages : illustrations (black and white) ; 23 cm. "**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." --Provided by the puublisher Contents: Part I Introduction to data mining -- Chapter 1 What's it all about? -- Chapter 2 Input: concepts, instances, attributes -- Chapter 3 Output: knowledge representation -- Chapter 4 Algorithms: the basic methods -- Chapter 5: Credibility: evaluating what's been learned -- Chapter 6 Preparation: data preprocessing and exploratory data analysis -- Chapter 7: Ethics: what are the impacts of what's been learned? -- Part II More advanced machine learning schemes -- Chapter 8 Ensemble learning -- Chapter 9 Extending instance-based and linear models -- Chapter 10 Deep learning: fundamentals -- Chapter 11 Advanced deep learning methods -- Chapter 12 Beyond supervised and unsupervised learning -- Chapter 13 Probabilistic methods: fundamentals -- Chapter 14 Advanced probabilistic methods -- Chapter 15 Moving on: applications and their consequences. Adult by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, and James R. Foulds. Includes bibliographical references and index. Purchased Ortega, Eric College of Computer Studies Computer Science Text in English Data mining 9780443158889 [paperback] University of Cebu-Banilad 260723 20260723104052.0