This text book is designed as a self contained, comprehensive study material for students, faculty and practicing professionals. It serves as a study material for all branches of engineering, technology and sciences. Concepts are explained in a simplified manner, followed by mathematical analysis and implementation of different Machine Learning algorithms.

• • Helps to prepare for interviews of Artificial Intelligence and Maschine Learning companies • Machine Learning Algorithms explained with necessary mathematical backgrounds • Separate chapters of Linear Algebra and Probability Theory which are essential for understanding and developing Machine Learning algorithms • Flowcharts and Python codes for Machine Learning algorithms provided • Each Chapter contains Descriptive Questions, MCQ's and Computer assignments • All chapters are supported with PPT's • Self learning exercises and projects • Suitable for various competitive exams - GATE, IES etc. • Machine Learning Laboratory experiments included to enhance skills as per National Education Policy

Forword / Preface / Acknowledgement / Introduction / Logistic Regression and Support Vector Machine / Decision Trees / Artificial Neural Networks / Clustering / K Nearest Neighbor Algorithm / Bayesian Networks / Linear Algebra for Machine Learning / Probability Theory for Machine Learning / Performance Analysis of Machine Learning Algorithms / Appendix / References.

Undergraduate and Postgraduate Students, Professional and Researchers