Machine Learning for Decision Makers, 2nd Edition

eBook Details:

• Paperback: 682 pages
• Publisher: WOW! eBook; 2nd edition (January 6, 2024)
• Language: English
• ISBN-10: 1484298004
• ISBN-13: 978-1484298008

eBook Description:

Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making, 2nd Edition

This new and updated Machine Learning for Decision Makers, 2nd Edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems.

This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes that have occurred in machine learning. A new chapter on quantum computers and machine learning is included to prepare you for future challenges. Insights for decision makers will help you understand machine learning and associated technologies and make efficient, reliable, smart, and efficient business decisions. All aspects of machine learning are covered, ranging from algorithms to industry applications. Wherever possible, required practical guidelines and best practices related to machine learning and associated technologies are discussed. Also covered in this edition are hot-button topics such as ChatGPT, superposition, quantum machine learning, and reinforcement learning from human feedback (RLHF) technology.

What You Will Learn

• Master the essentials of machine learning, AI, cloud, and the cognitive computing technology stack
• Understand business and enterprise decision-making using machine learning
• Become familiar with machine learning best practices
• Gain knowledge of quantum computing and quantum machine learning

Upon completing this Machine Learning for Decision Makers, 2nd Edition book, you will understand machine learning, IoT, and cognitive computing and be prepared to cope with future challenges related to machine learning.