Machine Learning for Business Applications | Pratyush Banerjee, Supriti Mishra & Shivashankar Chari | Business Applications Case Studies | Machine Learning Algorithms

Author: Pratyush Banerjee

Brand: McGraw-Hill Education

Edition: First Edition

Binding: paperback

Number Of Pages: 416

Release Date: 10-05-2025

Details: 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 to 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 Mining, and Ethics of Data Mining with AI, this book will appeal to a wider range of audiences.

Salient Features
Concise textbook 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 applications, application of Machine Learning in social media analytics, ethics of data mining with AI, etc.

Table of Contents
PART I: Introduction to Machine Learning with Python and Orange Chapter 1: Introduction to Python and Orange Chapter 2: Data Preparation and Data Transformation PART II: Supervised Machine Learning with Python and Orange Chapter 3: Fundamentals of Machine Learning Chapter 4: Supervised Machine Learning Chapter 5: Decision Tree and Ensemble Models PART III: Unsupervised Machine Learning and Deep Learning with Python and Orange Chapter 6: Unsupervised Machine Learning Chapter 7: Artificial Neural Networks and Deep Learning PART IV: Text Analytics Applications with Python and Orange Chapter 8: Text Analytics Chapter 9: Sentiment Analytics PART V: Data Accessibility and Ethical Issues for Machine Learning Applications Chapter 10: Ethical Issues of Using AI/ML Chapter 11: Social Media Analytics Index

EAN: 9789364443531

Package Dimensions: 9.4 x 7.1 x 1.8 inches

Languages: English