This book is a comprehensive introduction to the principles and techniques of machine learning. It covers key topics such as supervised and unsupervised learning, neural networks, decision trees, support vector machines, and clustering algorithms. The content is structured to guide readers from basic concepts to more advanced topics, ensuring a thorough understanding of both theoretical foundations and practical applications. Real-world examples, case studies, and hands-on exercises are included to illustrate the application of machine learning techniques in various domains such as finance, healthcare, and technology.
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Introduction to Machine Learning
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0Table of contents
- Cover
- Title Page
- Copyright
- About the Author
- Table of Contents
- List of Figures
- List of Tables
- Preface
- CHAPTER 1: INTRODUCTION TO MACHINE LEARNING
- CHAPTER 2: UNSUPERVISED LEARNING
- CHAPTER 3: SUPERVISED LEARNING: LINEAR REGRESSION
- CHAPTER 4: DECISION TREES
- CHAPTER 5: ARTIFICIAL NEURAL NETWORKS
- CHAPTER 6: REINFORCEMENT LEARNING
- CHAPTER 7: APPLICATIONS OF MACHINE LEARNING IN INDUSTRIAL SECTORS
- CHAPTER 8: ISSUES OF MACHINE LEARNING FOR SOCIETY
- INDEX
- Back Cover