165,41 €
183,79 €
-10% with code: EXTRA
Introduction to Algorithms for Data Mining and Machine Learning
Introduction to Algorithms for Data Mining and Machine Learning
165,41
183,79 €
  • We will send in 10–14 business days.
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…
183.79
  • Publisher:
  • ISBN-10: 0128172169
  • ISBN-13: 9780128172162
  • Format: 15.2 x 22.9 x 1 cm, minkšti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

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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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  • Author: Xin-She Yang
  • Publisher:
  • ISBN-10: 0128172169
  • ISBN-13: 9780128172162
  • Format: 15.2 x 22.9 x 1 cm, minkšti viršeliai
  • Language: English English

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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