Accuracy isn't everything. We’ll dive into Precision, Recall, and F1-Score for imbalanced datasets, and use ROC-AUC and Confusion Matrices to fully diagnose classifier performance.

Predict based on proximity. We’ll use K-Nearest Neighbors (KNN) for classification, understanding how distance metrics work and why the "curse of dimensionality" affects performance.

Mimic human decision-making. We’ll visualize tree structures, understand splitting criteria like Gini impurity, and learn how to prune trees to prevent overfitting.

Master the hyperplane. We’ll explore how SVMs use kernels (Linear, RBF, Poly) to project data into higher dimensions, allowing for precise separation of complex, non-linear classes.

Classify data with speed. We’ll compare Logistic Regression for probability estimation against the SGDClassifier, which efficiently handles massive datasets using gradient descent.

How good is your prediction? We’ll evaluate performance using standard metrics like RMSE and MAE, and learn when to use R-squared versus Median Absolute Error for different business goals.

Not all relationships are straight lines. Learn to capture curvature using Polynomial Features and Support Vector Regression (SVR) to model complex, non-linear data patterns.

Outliers can wreck linear models. We’ll use robust estimators like RANSAC and Theil-Sen that ignore anomalous data points to find the true underlying trend in noisy datasets.

Start with the basics. We’ll cover Ordinary Least Squares (OLS) and explore regularization techniques like Ridge (L2) and Lasso (L1) to prevent overfitting and perform feature selection.

Avoid redundant computation. We’ll enable caching in pipelines to store the results of expensive transformers, significantly speeding up iterative experiments and hyperparameter tuning.