Machine Learning Formulas & Concepts

Machine Learning Formulas & Concepts is the reference card for data scientists and ML engineers who need a quick reminder of formulas, algorithm properties, and evaluation metrics. What's included: - Supervised Learning Algorithm Reference - Algorithm Comparison Table (complexity, use cases, pros/cons) - Evaluation Metrics: Accuracy, Precision, Recall, F1, AUC-ROC - Regression Metrics: MSE, RMSE, MAE, R² - Loss Functions Reference - Optimization Algorithms: SGD, Adam, RMSprop - Regularization: L1, L2, Dropout - Cross-Validation Methods - Feature Engineering Reference - scikit-learn API Quick Reference - Statistical Tests Reference - Probability Distributions Reference