Official scikit-learn getting started tutorials and user guide for classical supervised/unsupervised learning, preprocessing, cross-validation, and evaluation metrics.
In Phase 2, you implement Linear Regression, Logistic Regression, k-NN, Decision Trees, and k-Means from scratch in NumPy—and then benchmark your custom implementations against scikit-learn's reference estimators. The official scikit-learn Getting Started guide and User Guide document the exact mathematical formulations, regularization penalties, and evaluation APIs.
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Phase 2 Reference & Verification Benchmark (Modules 2.1–2.4)
100% free open-source documentation and runnable examples.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 2: Machine Learning From Scratch
Use as your reference benchmark when verifying your from-scratch NumPy implementations of Linear/Logistic Regression, Decision Trees, PCA, and k-Means.
Learning paradigms, train/validation/test splits, parameters vs. hyperparameters, overfitting, the bias-variance tradeoff, regularization, and data leakage prevention.
Mathematical formulation and NumPy implementation of linear regression, logistic regression, MSE, binary cross-entropy, and decision boundaries.
K-Nearest Neighbors, Decision Trees, Random Forests, Naive Bayes, SVMs, K-Means Clustering, PCA, and Gradient Boosting.
Classification and regression metrics, confusion matrices, precision-recall tradeoffs, ROC-AUC, baseline comparisons, and leakage detection.
Google's practical, interactive introduction to machine learning fundamentals, linear and logistic regression, gradient descent, generalization, regularization, and neural networks.
Practical interactive course covering missing values, categorical variables, pipelines, cross-validation, gradient boosting (XGBoost), and data leakage.
Learn numerical arrays, broadcasting, vectorized computation, matrix operations, and scientific Python fundamentals.