Machine Learning

113 resources

Machine learning sits on linear algebra, probability and some calculus, so it helps to review those alongside the first course. Andrew Ng's Machine Learning Specialization and Google's Machine Learning Crash Course are common starting points, and An Introduction to Statistical Learning explains core methods like regression, trees and cross-validation without heavy math.

For deeper theory, Pattern Recognition and Machine Learning by Christopher Bishop and The Elements of Statistical Learning are standard references, and Mathematics for Machine Learning fills gaps in the underlying math. Deep Learning by Goodfellow, Bengio and Courville covers neural networks. Practice matters as much as reading: implement a few algorithms yourself before relying on scikit-learn.

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Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron CourvilleBook

Made with ML

Goku MohandasApplied ML · MLOps · ProductionCourse

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