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What Every Engineer Should Know About Data-Driven Analytics

By: (Author) Phillip A. Laplante , (Author) Satish Mahadevan Srinivasan

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Ksh 24,300.00

Format: Hardback or Cased Book

ISBN-10: 1032235438

ISBN-13: 9781032235431

Series: What Every Engineer Should Know

Publisher: Taylor & Francis Ltd

Imprint: CRC Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Apr 13th, 2023

Print length: 260 Pages

Weight: 534 grams

Dimensions (height x width x thickness): 24.00 x 16.30 x 2.10 cms

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What Every Engineer Should Know About Data-Driven Analytics provides a comprehensive introduction to the machine learning theoretical concepts and approaches that are used in predictive data analytics through practical applications and case studies.

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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