Machine Learning for Decision Makers
Apress (Verlag)
978-1-4842-9800-8 (ISBN)
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.
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
Who This Book Is For
Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them
Patanjali Kashyap PhD works as a technology manager at a leading American bank. He deals with high-impact, mission-critical financial and innovative new generation technology projects on a day-to-day basis. Dr Kashyap has worked with technology giants such as Infosys and Cognizant Technology Solutions. He is an expert in the Agile process, machine learning, big data, and cloud computing. He understands Microsoft Azure and cognitive computing platforms such as Watson and Microsoft Cognitive Services. Dr Kashyap introduces .NET technologies as his first love to his friends and colleagues. He has worked on a spectrum of .NET and associated technologies such as SQL Server and component-based architecture from their inception. He also loves to work on SharePoint (content management, in general), knowledge management, positive technology, psychological computing, and UNIX. Dr Kashyap is experienced in software development methodologies, application support, and maintenance. Hehas published several research and white papers on multiple topics. He is involved in organizational initiatives such as building world class teams and dynamic cultures across enterprises. And he is a go-to person for incorporating “positivity and enthusiasm” in enterprises.
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.
| Erscheinungsdatum | 27.12.2023 |
|---|---|
| Zusatzinfo | 13 Illustrations, color; 32 Illustrations, black and white; XXI, 661 p. 45 illus., 13 illus. in color. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Algorithm analysis and problem complexity • Artificial Neural Networks • Bayesian networks • Big Data • Cloud • cognitive computing • decision trees • IOT • machine learning • security • Vector Machines |
| ISBN-10 | 1-4842-9800-4 / 1484298004 |
| ISBN-13 | 978-1-4842-9800-8 / 9781484298008 |
| Zustand | Neuware |
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