Artificial Intelligence has been one of the fascinating technology trends of the 21st century. Today almost all sectors and industries embrace the benefits of AI technology for a wide range of applications, including business process automation, predictive analysis, enhanced customer experience, increased productivity, reduction in costs, etc.
So if you’re a data scientist, researcher, data engineer, business leader, or just someone who’s interested in this industry, here we’ve picked up a list of some of the best AI, deep learning and machine learning books for beginners and experts.
Best Machine Learning Books
This is a great machine learning book if you are new to machine learning or want to extend your knowledge in this field. Python Machine Learning is a comprehensive introduction to machine learning packed with clear explanations and working examples. Including the popular TensorFlow deep learning library, the book combines theory with practice covering all the essential machine learning techniques in depth.
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurelien Geron
In Aurelien Geron’s book, you’ll start with machine learning fundamentals, then move on to more advanced applications and algorithms. The book is packed with practical examples using Python frameworks-Scikit-Learn and TensorFlow. In this book, you’ll learn simple linear regression and moving to deep neural networks. The book will also teach you all the techniques and tools you need to build intelligent systems. In addition, the examples and exercises in each chapter will help you apply what you’ve learned.
The Hundred-Page Machine Learning Book is perhaps one of the best machine learning books. It serves as a great introduction to all major machine learning concepts, ranging from supervised and unsupervised learning, deep learning, neural networks, support vector machines, classical linear and logistic regression, etc. The book also illustrates some of the algorithms using Python coding language. The Hundred-Page Machine Learning Book is recommended both for beginners and experienced practitioners looking to enhance their knowledge base.
The book explores the basic concepts of machine learning and pattern recognition to present the Bayesian viewpoint. In this book, you’ll learn statistical pattern recognition techniques and get familiar with graphical models and inference methods for describing probability distributions. Knowledge of basic linear algebra and multivariate calculus is needed for going through the book.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, Jerome Friedman
This is a valuable resource taking you deep into data mining and statistical pattern recognition. The book uses many special cases and real data examples, explaining clearly the relationship between methods. It includes both statistical approaches and modern tools for data analysis necessary for the practical implementation of the methods.