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Autor(en): 
  • Satish Mahadevan Srinivasan
  • Laplante Phillip A.
  • What Every Engineer Should Know About Data-Driven Analytics 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  April 2023  
    Genre:  EDV / Informatik 
     
    advanced machine learning applications / Big Data System / Boston Housing Dataset / CNN Architecture / CNN Model / COMPUTERS / Computer Engineering / COMPUTERS / Computer Science / COMPUTERS / Data Science / Machine Learning
    ISBN:  9781032235400 
    EAN-Code: 
    9781032235400 
    Verlag:  Taylor and Francis 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 156 mm / D  
    Gewicht:  440 gr 
    Seiten:  260 
    Illustration:  schwarz-weiss Illustrationen, Zeichnungen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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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