Essential machine learning formulas covering supervised learning (linear regression, logistic regression, SVM, decision trees), unsupervised learning (k-means, PCA), neural networks (backpropagation, activation functions), regularization techniques, evaluation metrics, and optimization algorithms like gradient descent. 6 pages | 40 formulas | Topics: Regression, Classification, Neural Networks, Evaluation Metrics, Optimization | Course codes: CS 229, CS 4780, EECS 445, CSE 446, CSCI 5525 Print-ready PDF. Available in A4 and US Letter sizes. Instant download after purchase.