Supervised Learning
Prediction with labeled data: linear models, trees, SVMs, and more.
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Linear Models
Linear and logistic regression, coefficient interpretation, and multicollinearity.
Regularization
L1 vs L2 geometry and their effect on bias and variance.
Tree-Based Models
Decision trees, Random Forests, gradient boosting, and splitting criteria.
Other Models
SVM, KNN, Naive Bayes, and related intuition.
Data Issues
Imbalanced data and other supervised learning data problems.