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What Every Engineer Should Know About Data-Driven Analytics

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What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains.

  • Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making
  • Introduces various approaches to build models that exploits different algorithms
  • Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets
  • Explores the augmentation of technical and mathematical materials with explanatory worked examples
  • Includes a glossary, self-assessments, and worked-out practice exercises

Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.

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What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the theoretical concepts and approaches of machine learning that are used in predictive data analytics. By introducing the theory and by providing practical applications, this text can be understood by every engineering discipline. It offers a detailed and focused treatment of the important machine learning approaches and concepts that can be exploited to build models to enable decision making in different domains.

  • Utilizes practical examples from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making
  • Introduces various approaches to build models that exploits different algorithms
  • Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets
  • Explores the augmentation of technical and mathematical materials with explanatory worked examples
  • Includes a glossary, self-assessments, and worked-out practice exercises

Written to be accessible to non-experts in the subject, this comprehensive introductory text is suitable for students, professionals, and researchers in engineering and data science.

Produktdetaljer
Sprog: Engelsk
Sider: 260
ISBN-13: 9781032235400
Indbinding: Paperback
Udgave:
ISBN-10: 1032235403
Udg. Dato: 13 apr 2023
Længde: 19mm
Bredde: 155mm
Højde: 234mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 13 apr 2023
Forfatter(e) Satish Mahadevan (Penn State Great Valley Srinivasan, Phillip A. Laplante, Phillip A. (The Pennsylvania State University Laplante, Satish Mahadevan Srinivasan


Kategori Automatisk styrings- & reguleringsteknik


Sprog Engelsk


Indbinding Paperback


Sider 260


Udgave


Længde 19mm


Bredde 155mm


Højde 234mm


Udg. Dato 13 apr 2023


Oplagsdato 13 apr 2023

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