ujjwalkarn/Machine-Learning-Tutorials

Organizes curated learning resources across 20+ ML/DL topics—from foundational algorithms (regression, SVMs, decision trees) to specialized architectures (CNNs, RNNs, autoencoders) and applications (NLP, computer vision, reinforcement learning). Aggregates links to university courses (Stanford, MIT, Berkeley), research papers, interview prep materials, and framework-specific tutorials rather than implementing algorithms directly. Cross-references complementary Python and R-focused resource lists to guide learners toward language-specific implementations.

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