MACHINE LEARNING UNIVERSITY NOTES

 






Resource / Course




What you get / Good for
CS229: Machine Learning (by Andrew Ng — lecture notes PDF) A classic and comprehensive course — covers supervised & unsupervised learning, SVMs, learning theory, optimization, and more. Great as both reference and a structured learning path. (cs229.stanford.edu)
STAT 479: Machine Learning (by Sebastian Raschka) — lecture notes PDF Clean, concise overview of ML fundamentals (supervised/unsupervised learning, basic algorithms) — good for revision or quick study. (sebastianraschka.com)
MIT 6.036: Introduction to Machine Learning — lecture notes PDF Offers solid foundational coverage of ML concepts and algorithms, suitable for undergrads or self-study. (Phillip Isola)
Introduction to Machine Learning (by Ethem Alpaydin) — 2nd Edition PDF Well-organized textbook covering supervised learning, Bayesian methods, parametric/non-parametric models, multivariate methods — a good mid-level resource. (erp.metbhujbalknowledgecity.ac.in)

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