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Autor(en): 
  • Sanjeet Kumar Dwivedi
  • Vikas Khare
  • Monica Bhatia
  • Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2024  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / COMPUTERS / Data Science / Data Visualization / COMPUTERS / Database Administration & Management / Data Capture & Analysis / Data capture and analysis
    ISBN:  9780443160783 
    EAN-Code: 
    9780443160783 
    Verlag:  Elsevier 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 191 mm / D  
    Gewicht:  690 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.

    The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

      



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