10 Underrated Books for Mastering Machine Learning
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Explore ten underrated books that delve deeper into machine learning theory and practice. These books range from mathematical foundations to practical applications, aiding in the advancement of your understanding of Bayesian methods, statistical learning, and deep learning frameworks.
Table of contents
1. Pattern Recognition and Machine Learning by Christopher M. Bishop2. The Elements of Statistical Learning by Hastie, Tibshirani, & Friedman3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy4. Bayesian Reasoning and Machine Learning by David Barber5. Learning from Data by Yaser S. Abu-Mostafa6. Information Theory, Inference, and Learning Algorithms by David MacKay7. Understanding Machine Learning by Shai Shalev-Shwartz & Shai Ben-David8. Mathematics for Machine Learning by Marc Peter Deisenroth, et al.9. Neural Networks and Deep Learning by Michael Nielsen10. Machine Learning for Hackers by Drew Conway & John Myles WhiteWrapping UpSort: