This text covers all the fundamentals and presents basic theoretical concepts and a wide range of techniques (algorithms) applicable to challenges in our day-to-day lives. The book recognizes that most of the ideas behind machine learning are simple and straightforward. It provides a platform for hands-on experience through self-study machine learning projects. Datasets for some benchmark applications have been explained to encourage the use of algorithms covered in this book. This is a comprehensive text book on machine learning for undergraduates in computer science and all engineering degree programs. Post graduates and research scholars will find it a useful initial exposure to the subject, before they go for highly theoretical depth in the specific areas of their research. For engineers, scientists, business managers and other practitioners, the book will help build the foundations of machine learning.
Madan Gopal, Ex-Professor, IIT Delhi
1. Introduction 2. Supervised Learning: Rationale and Basics 3. Statistical Learning 4. Learning With Support Vector Machines (SVM) 5. Learning With Neural Networks (NN) 6. Fuzzy Inference Systems 7. Data Clustering and Data Transformations 8. Decision Tree Learning 9. Business Intelligence and Data Mining: Techniques and Applications ? Appendix A Genetic Algorithm (GA) For Search Optimization ? Appendix B Reinforcement Learning (RL) ? Datasets from Real-Life Applications for Machine Learning Experiments
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