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IGI Global – Encyclopedia of Data Science and Machine Learning 2022

Updated August 10, 2026 90.3 MB
IGI Global – Encyclopedia of Data Science and Machine Learning 2022

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Description

The Encyclopedia of Data Science and Machine Learning is a comprehensive reference that reviews the latest advances and research in data science, machine learning, and artificial intelligence. It is designed to provide an international forum for practitioners and researchers to advance the knowledge and practical applications of these emerging fields in modern business and scientific communities.

The content of this encyclopedia includes 187 specialized chapters written by more than 370 leading authors and scientists from 46 countries around the world. The book emphasizes the emerging theories, models, and processes that form the basis of the Fourth Industrial Revolution and seeks to optimize information systems and improve the health and wealth of societies through big data.

Book Features

  • Extensive coverage of topics such as data mining, optimization, statistics, and knowledge-based systems.
  • Providing practical solutions for using artificial intelligence in sustainable development and business management.
  • Exploring specific applications such as cancer diagnosis, change management, and global software development.
  • Includes in-depth scientific content about Big Data systems and causal analysis.
  • Suitable for data scientists, technical managers, analysts, and graduate students.

Book specifications

  • Publisher: IGI Global
  • Instructor/Author: John Wang
  • Number of pages: 3296
  • Number of chapters: 56
  • Format: PDF

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Encyclopedia of Data Science and Machine Learning

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Encyclopedia of Data Science and Machine Learning

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What is included

  • Extensive coverage of topics such as data mining, optimization, statistics, and knowledge-based systems.
  • Providing practical solutions for using artificial intelligence in sustainable development and business management.
  • Exploring specific applications such as cancer diagnosis, change management, and global software development.
  • Includes in-depth scientific content about Big Data systems and causal analysis.
  • Suitable for data scientists, technical managers, analysts, and graduate students.