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Viser: Introduction to Algorithms for Data Mining and Machine Learning
Introduction to Algorithms for Data Mining and Machine Learning Vital Source e-bog
Xin-She Yang
(2019)
Introduction to Algorithms for Data Mining and Machine Learning
Xin-She Yang
(2019)
Sprog: Engelsk
Detaljer om varen
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Elsevier Science (Juni 2019)
- ISBN: 9780128172179
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
- Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
- Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
- Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
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Detaljer om varen
- Paperback: 188 sider
- Udgiver: Elsevier Science & Technology (Juni 2019)
- ISBN: 9780128172162
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
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