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Machine Learning for Decision Makers [electronic resource] : Cognitive Computing Fundamentals for Better Decision Making / by Patanjali Kashyap.

By: Contributor(s): Material type: TextPublisher: Berkeley, CA : Apress : Imprint: Apress, 2024Edition: 2nd ed. 2024Description: XXI, 661 p. 45 illus., 13 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781484298015
Subject(s):
Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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.

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