Fundamentals of Machine Learning: A Business Perspective
1st Edition
9364442067
·
9789364442060
© 2026 | Published: April 9, 2026
About the Book:This book aims to serve as a textbook for a course on Machine Learning for Business Applications and related courses. It provides a comprehensive overview of how machine learning and deep learning can be used in various business analyt…
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Chapter 1: Introduction to Python and Orange
Chapter 2: Data Preparation and Data Transformation
Chapter 3: Fundamentals of Machine Learning
Chapter 4: Supervised Machine Learning
Chapter 5: Decision Tree
Chapter 6: Unsupervised Machine Learning
Chapter 7: Text Analytics
Chapter 8: Sentiment Analytics
Chapter 9: Ethical Issues of Using AI/ML
Chapter 10: Social Media Analytics
Index
Chapter 2: Data Preparation and Data Transformation
Chapter 3: Fundamentals of Machine Learning
Chapter 4: Supervised Machine Learning
Chapter 5: Decision Tree
Chapter 6: Unsupervised Machine Learning
Chapter 7: Text Analytics
Chapter 8: Sentiment Analytics
Chapter 9: Ethical Issues of Using AI/ML
Chapter 10: Social Media Analytics
Index
About the Book:
This book aims to serve as a textbook for a course on Machine Learning for Business Applications and related courses. It provides a comprehensive overview of how machine learning and deep learning can be used in various business analytics situations. It equips the reader with the understanding of various tools like Jupyter Notebook (for python coding), and Orange (python-based GUI interface). This will help the reader understand the application of various algorithms for performing analysis by using different real-life case studies and examples. With coverage of topics like Social Media Analytics, Text Analytics, and Ethical Issues of Using AI/ML, this book will appeal to a wider audience.
Key Features:
• Concise textbooks with case studies related to various business applications like Financial Analytics, HR Analytics, etc.
• Provides detailed, step-by-step understanding about implementation of various machine learning algorithms
• Demonstrates the practical applicability of different algorithms with the use of examples, which will help students who require practical illustrations of how these algorithms might be used in actual business analytics situations
• Covers some of the important topics like text mining concepts and application, application of Machine Learning in social media analytics, ethics of data mining with AI, etc.
This book aims to serve as a textbook for a course on Machine Learning for Business Applications and related courses. It provides a comprehensive overview of how machine learning and deep learning can be used in various business analytics situations. It equips the reader with the understanding of various tools like Jupyter Notebook (for python coding), and Orange (python-based GUI interface). This will help the reader understand the application of various algorithms for performing analysis by using different real-life case studies and examples. With coverage of topics like Social Media Analytics, Text Analytics, and Ethical Issues of Using AI/ML, this book will appeal to a wider audience.
Key Features:
• Concise textbooks with case studies related to various business applications like Financial Analytics, HR Analytics, etc.
• Provides detailed, step-by-step understanding about implementation of various machine learning algorithms
• Demonstrates the practical applicability of different algorithms with the use of examples, which will help students who require practical illustrations of how these algorithms might be used in actual business analytics situations
• Covers some of the important topics like text mining concepts and application, application of Machine Learning in social media analytics, ethics of data mining with AI, etc.