Machine Learning Introduction - Best Books on Machine Learning v 1.0
The most popular book: Python Machine Learning, second edition by Sebastian Raschka and Vahid Mirjalili, is a tutorial to a broad range of machine learning applications with Python. It provides a practical introduction to machine learning using popular libraries like SciPy, NumPy, scikit-learn, Matplotlib, and pandas.
Pattern Recognition and Machine Learning by Christopher M. Bishop. This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. This is a great book for beginners looking to step into Machine Learning.
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville explores a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. "Written by three experts in the field, Deep Learningis the only comprehensive book on the subject." - Elon Musk
Machine Learning A Probabilistic Perspective by Kevin Murphy relies on MATLAB for the models it describes and is very rule-based heavy. Probability, Optimization and Linear Algebra are explored and materially and this tends to be a favorite among Wall Street quants.
Machine Learning Discriminative and Generative by Tony Jebara
Excellent book that discusses its two subjects in depth while showing their connections. Fantastic background on many of the most important Machine Learning algorithms.
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