This book is designed for undergraduates, postgraduates and professionals who want to have a firm grip on the fundamental principles of AI and ML.

Artificial Intelligence (AI) is a broad area of knowledge which has percolated into every aspect of human life. ‘Machine Learning algorithms’ are considered to be a subset of AI Theory, mathematics and coding are three aspects to any topic in AI. This book covers the most relevant topics in the field of Artificial Intelligence and Machine Learning (ML). 

The subdivisions of Machine Learning are Supervised, Unsupervised and Reinforcement learning. All three are covered in sufficient depth. One very important and upcoming field of application is Natural Language Processing (NLP). A whole section of the book has been devoted to this. 

The book covers the conceptual, mathematical and numerical analysis of the important ML algorithms and their practical applications. The topics covered include AI search algorithms, Classical machine learning, Deep learning theory and popular networks, Natural Language Processing (NLP) and Reinforcement learning. Numerical examples and lucid explanations give the reader an easy entry into the world of AI and ML.