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Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making

Autor Patanjali Kashyap
en Limba Engleză Paperback – 23 dec 2023
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.


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

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Specificații

ISBN-13: 9781484298008
ISBN-10: 1484298004
Pagini: 661
Ilustrații: XXI, 661 p. 45 illus., 13 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.94 kg
Ediția:Second Edition
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.



Notă biografică

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. 

Textul de pe ultima copertă

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




Caracteristici

Covers machine learning algorithms and techniques used to solve problems in the industry Discusses quantum computing concepts and applications Covers practical guidelines and best practices for machine learning