Cognitive Computing Fundamentals for Better Decision Making Kashyap, Patanjali. creator author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text xxu 2024 2nd ed. 2024. monographic eng

electronic

electronic resource

access XXI, 661 p. 45 illus., 13 illus. in color. online resource. This new and updated 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. Upon completing this book, you will understand machine learning, IoT, and cognitive computing and be prepared to cope with future challenges related to machine learning. You will: Learn 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. Chapter 1: Let’s Integrate with Machine Learning -- Chapter 2: The Practical Concepts of Machine Learning -- Chapter 3: Machine Learning Algorithms and Their Relationship with Modern Technologies -- Chapter 4: Technology Stack for Machine Learning and Associated Technologies -- Chapter 5: Industrial Applications of Machine Learning -- Chapter 6: I Am the Future: Machine Learning in Action -- Chapter 7: Innovation, KPIs, Best Practices, and More for Machine Learning -- Chapter 8: Do Not Forget Me: The Human Side of Machine Learning -- Chapter 9: Quantum Computers and Machine Learning -- Chapter 10: Let’s Wrap Up: The Final Destination. by Patanjali Kashyap. Artificial intelligence Software engineering Algorithms Artificial Intelligence Software Engineering Algorithms Q334-342 TA347.A78 9781484298015 https://doi.org/10.1007/978-1-4842-9801-5 https://doi.org/10.1007/978-1-4842-9801-5 231222 20251030110817.0 978-1-4842-9801-5