∑ Math for AI

Stephen Welch's elegant 7-part series demystifying neural networks — math and intuition. {description}

StatQuest's stats series — distributions, p-values, ANOVA, regression. Stats foundations for ML. {description}

Josh Starmer's StatQuest — clearly explains every ML algorithm with cartoons and BAM! moments. {description}

3Blue1Brown's geometric intuition for linear algebra — vectors, matrices, eigenvalues. Foundation for understanding ML. {description}

Linear algebra, calculus, probability, and statistics — the math foundations of ML. {description}

NPTEL's IISc Bangalore course on pattern recognition and ML — Bayesian methods, SVMs, neural networks. {description}

Prof. Balaraman Ravindran's NPTEL course on ML fundamentals — one of India's most popular ML courses. {description}

Andrew Ng's legendary Stanford ML course — supervised/unsupervised learning, learning theory, reinforcement learning. {description}

Gilbert Strang's celebrated course on the linear algebra foundations of ML — SVD, PCA, optimization, neural nets. {description}