MACHINE LEARNING

This book on Machine Learning is designed every bit a textbook for undergraduate as well as mail service-graduate students of applied science. It provides a comprehensive coverage of fundamentals of auto learning. Spread over xvi chapters, the volume starts alongside an overview of automobile learning and discusses the need for understanding information as well as necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, in addition to determination dominion-based classification algorithms. The volume provides an introduction to Bayesian learning in addition to probabilistic graphical models. Important topics such equally back up vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, as well as genetic algorithms are discussed in depth. It ends with the latest developments inward deep learning. A perfect residue betwixt theoretical too mathematical exposition is provided with several numerical examples, review questions, and Python programs. It volition as well live useful for technology professionals too information technology employees who want to learn the basics of the discipline. Key features. Adopts an ‘Algorithmic Approach’ to illustrate the concepts of car learning in a uncomplicated linguistic communication with 100+ numerical problems. Adapts ‘Minimal Mathematics Strategy’ with more than emphasis on agreement the basics of motorcar learning. Has ‘Comprehensive Coverage’ of all topics that are relevant to auto learning alongside 100+ figures and Python codes. Provides ‘Simple Explanation’ to topics such equally clustering, back up vector machines, genetic algorithms, artificial neural networks, ensemble learning, in addition to deep learning. Contains ‘Appendices’ that talk over the basics of Python and Python packages such as NumPy, Pandas, Scikit-larn, Matplotlib, SciPy, too Keras. Includes a ‘Laboratory Manual’ alongside examples illustrated through Python in addition to its packages. Comes amongst ‘Useful Pedagogical Features’ such equally Crossword as well as Word Search Online Resources The next 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 empathize the subject amend Table of contents 1. Introduction to Machine Learning two. Understanding Data iii. Basics of Learning Theory 4. Similarity-based Learning five. Regression Analysis half-dozen. Decision Tree Learning 7. Rule–based Learning 8. Bayesian Learning ix. Probabilistic Graphical Models 10. Artificial Neural Networks 11. Support Vector Machines 12. Ensemble Learning thirteen. Clustering Algorithms xiv. Reinforcement Learning xv. Genetic Algorithms 16. Deep Learning