Description
Mcgraw Hill Education Machine Learning For Business Applications by Banerjee
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 FeaturesConcise 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 ContentsPART I: Introduction to Machine Learning with Python and OrangeChapter 1: Introduction to Python and OrangeChapter 2: Data Preparation and Data TransformationPART II: Supervised Machine Learning with Python and OrangeChapter 3: Fundamentals of Machine LearningChapter 4: Supervised Machine LearningChapter 5: Decision Tree and Ensemble ModelsPART III: Unsupervised Machine Learning and Deep Learning with Python and OrangeChapter 6: Unsupervised Machine LearningChapter 7: Artificial Neural Networks and Deep LearningPART IV: Text Analytics Applications with Python and OrangeChapter 8: Text AnalyticsChapter 9: Sentiment AnalyticsPART V: Data Accessibility and Ethical Issues for Machine Learning ApplicationsChapter 10: Ethical Issues of Using AI/MLChapter 11: Social Media Analytics