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Artificial Intelligence is the new cool. It is a very wide topic and includes machine learning, knowledge representation, reason, etc. If you took up Computer Science and Mathematics in high school, congratulations, you have just made the task a little bit easier. The knowledge of basic CS and Math is important for Machine Learning and AI. 

The views on Wikipedia and Quora about ML are very high and that shows the interest of the generation in the future. This article will mention some books that will help you understand better the art of Machine Learning better. 

Written by the CS professor at the University of Edinburg this book depicts the dramatic growth in practical applications for machine learning over the last ten years. This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is apt for undergraduate students as well as student doing their Ph.D. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. Also recommended by Forbes, the book is suitable for courses on machine learning, statistics, CS, signal processing, computer vision, data mining, and bioinformatics.  

This books starts from the basics and takes you on a ride of probabilistic graphical methods that are important for Artificial Intelligence. The framework of probabilistic graphical models, presented in this book, provides a general approach to reach conclusions based on available information. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques including applications in computer vision, robotics, natural language understanding, and computational biology. 

Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David 

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. 

'Hands-on Machine Learning with Scikit-Learn and TensorFlow' by Aurélien Géron 

Published in 2017, this graphical in black and white displays AI through a series of recent breakthroughs, and shows how deep learning has boosted the entire field of machine learning. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. 

It is a book on comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. 

Available on Amazon- 70 USD 

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