Practical machine learning tools and techniques Witten, I. H. (Ian H.) creator author Frank, Eibe author Hall, Mark A. (Mark Andrew) author Pal, Christopher J. author Foulds, James R. author text us United States Elsevier Morgan Kaufmann 2026 Fifth edition. monographic eng xl 760 pages : illustrations. Part I. Introduction to data mining -- What's it all about? -- Input: concepts, instances, attributes -- Output: knowledge representation -- Algorithms: the basic methods -- Credibility: evaluating what's been learned -- Preparation: data preprocessing and exploratory data analysis -- Ethics: what are the impacts of what's been learned? -- Part II. More advanced machine learning schemes -- Ensemble learning -- Extending instance-based and linear models -- Depp learning: fundamentals -- Advanced deep learning methods -- Beyond supervised and unsupervised learning -- Probabilistic methods: fundamentals -- Advanced probabilistic methods -- Moving on: applications and their consequences. specialized Includes bibliographical references and index. Data mining Minería de datos 9780443158889 https://itmsp.museknowledge.com/UOH?qurl=https://libros-uoh.uoh.cl/ebook_2/Data-miningpractical-machine-learning-tools-and-techniques https://itmsp.museknowledge.com/UOH?qurl=https://libros-uoh.uoh.cl/ebook_2/Data-miningpractical-machine-learning-tools-and-techniques clrauoh 251216 20260410160724.0 eng