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Introduction to Algorithms for Data Mining and Machine Learning - Xin-She (School of Science and Technology Yang - Bog

Bag om Introduction to Algorithms for Data Mining and Machine Learning

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-studyProvides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780128172162
  • Indbinding:
  • Paperback
  • Sideantal:
  • 188
  • Udgivet:
  • 19. juni 2019
  • Størrelse:
  • 227x153x19 mm.
  • Vægt:
  • 322 g.
  • 8-11 hverdage.
  • 2. december 2024

Normalpris

  • BLACK NOVEMBER

Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Introduction to Algorithms for Data Mining and Machine Learning

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-studyProvides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

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