Cognitive science, computational intelligence, and data analytics

• Verfasst von: Khare, Vikas [VerfasserIn]
• Verfasst von: Dwivedi, Sanjeet Kumar [VerfasserIn]
• Verfasst von: Bhatia, Monica [VerfasserIn]
• Titel: Cognitive science, computational intelligence, and data analytics
• Titelzusatz: methods and applications with Python
• Verf.angabe: Vikas Khare, Sanjeet Kumar Dwivedi, Monica Bhatia
• Ausgabe: [First edition].
• Verlagsort: Cambridge, MA
• Verlag: Morgan Kaufmann
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (332 pages)
• Illustrationen: illustrations
• Fussnoten: Includes bibliographical references and index
• ISBN: 978-0-443-16079-0
• ISBN: 0-443-16079-1
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Exploration de données (Informatique)
• Sach-SW: Apprentissage automatique
• Sach-SW: Intelligence artificielle
• Sach-SW: artificial intelligence
• K10plus-PPN: 1892757974
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4544423481
• Lokale URL Inst.: Zum Volltext

Abstract

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.