Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python
Khare, Vikas, Dwivedi, Sanjeet Kumar, Bhatia, Monica
- 出版商: Morgan Kaufmann
- 出版日期: 2024-06-14
- 售價: $4,350
- 貴賓價: 9.5 折 $4,133
- 語言: 英文
- 頁數: 332
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0443160783
- ISBN-13: 9780443160783
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相關分類:
Python、程式語言、Data Science
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相關主題
商品描述
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.
商品描述(中文翻譯)
《認知科學、計算智能和數據分析:Python方法和應用》介紹了讀者基礎的數據分析、認知科學和計算智能概念,包括人工智能和機器學習。本書的重點在於基本思想、程序和計算智能工具,這些工具可以應用於各種數據分析方法,包括數學編程、演化模擬、機器學習和基於邏輯的模型。它向讀者介紹了認知科學和數據分析的基本和實用方面,探討了數據分析在描述、演化和實際問題應用方面的可行性。
作者們涵蓋了認知分析的歷史和演變、哲學中的方法論問題、語法和語義、生成語言學的理解、記憶理論和處理理論、結構化和非結構化數據、定性和定量數據、變量的測量、名義、順序、間隔和比例尺數據。本書的內容根據讀者的需求進行了定制,包括多變量分析、CRISP方法和SEMMA方法的介紹。每一章都提供了實際的、實踐性的學習,包括與所介紹的關鍵概念相關的案例研究和Python程序。