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                                                                  MACHINE LEARNING

This book on Machine Learning is designed as a textbook for undergraduate and post-graduate students of engineering. It provides a comprehensive coverage of fundamentals of machine learning. Spread over 16 chapters, the book starts with an overview of machine learning and discusses the need for understanding data and necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, and decision rule-based classification algorithms. The book provides an introduction to Bayesian learning and probabilistic graphical models. Important topics such as support vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, and genetic algorithms are discussed in depth. It ends with the latest developments in deep learning. A perfect balance between theoretical and mathematical exposition is provided with several numerical examples, review questions, and Python programs. It will also be useful for engineering professionals and IT employees who want to learn the basics of the subject. Key features. Adopts an ‘Algorithmic Approach’ to illustrate the concepts of machine learning in a simple language with 100+ numerical problems. Adapts ‘Minimal Mathematics Strategy’ with more emphasis on understanding the basics of machine learning. Has ‘Comprehensive Coverage’ of all topics that are relevant to machine learning with 100+ figures and Python codes. Provides ‘Simple Explanation’ to topics such as clustering, support vector machines, genetic algorithms, artificial neural networks, ensemble learning, and deep learning. Contains ‘Appendices’ that discuss the basics of Python and Python packages such as NumPy, Pandas, Scikit-learn, Matplotlib, SciPy, and Keras. Includes a ‘Laboratory Manual’ with examples illustrated through Python and its packages. Comes with ‘Useful Pedagogical Features’ such as Crossword and Word Search Online Resources The following resources are available to support the faculty and students using this book: For faculty:. Chapter PPTs. Solution Manual For students:. Python Programs. Lab Manual. Crosswords and Word Search to understand the subject better Table of contents 1. Introduction to Machine Learning 2. Understanding Data 3. Basics of Learning Theory 4. Similarity-based Learning 5. Regression Analysis 6. Decision Tree Learning 7. Rule–based Learning 8. Bayesian Learning 9. Probabilistic Graphical Models 10. Artificial Neural Networks 11. Support Vector Machines 12. Ensemble Learning 13. Clustering Algorithms 14. Reinforcement Learning 15. Genetic Algorithms 16. Deep Learning


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