04254nam a22005295i 4500 978-1-4842-9801-5 DE-He213 20251030110817.0 cr nn 008mamaa 231222s2024 xxu| s |||| 0|eng d 9781484298015 978-1-4842-9801-5 10.1007/978-1-4842-9801-5 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Kashyap, Patanjali. author. aut http://id.loc.gov/vocabulary/relators/aut 66182 Machine Learning for Decision Makers [electronic resource] : Cognitive Computing Fundamentals for Better Decision Making / by Patanjali Kashyap. 2nd ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XXI, 661 p. 45 illus., 13 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Artificial intelligence. 58441 Software engineering. 33953 Algorithms. 58677 Artificial Intelligence. 58441 Software Engineering. 33953 Algorithms. 58677 SpringerLink (Online service) Springer Nature eBook Printed edition: 9781484298008 Printed edition: 9781484298022 Printed edition: 9798868806872 https://doi.org/10.1007/978-1-4842-9801-5 ZDB-2-CWD ZDB-2-SXPC 56313 56313