Best Machine Learning Books for Beginners & Experts

The book is a must-read for every JavaScript novice and is excellent for anyone who wants to learn JavaScript.  

"Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron:  

Written by one of the pioneers of machine learning, this book offers practical advice and insights for beginners looking to build real-world machine learning systems. 

"Machine Learning Yearning" by Andrew Ng: 

This comprehensive book covers the fundamentals of pattern recognition and machine learning algorithms, making it suitable for beginners with a mathematical background. 

"Pattern Recognition and Machine Learning" by Christopher M. Bishop: 

This book introduces machine learning concepts using Python and covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and deep learning. 

"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili: 

This book provides an in-depth exploration of deep learning techniques, including neural networks, convolutional networks, and recurrent networks. It is suitable for experts in the field. 

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville:

This book is a comprehensive guide to statistical learning and covers a wide range of topics, including linear regression, support vector machines, and tree-based methods. 

"The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: 

This book focuses on Bayesian statistical methods and their application to machine learning. It covers concepts such as hierarchical models, Markov chain Monte Carlo, and Bayesian regression. 

"Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin: 

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