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Classic Machine Learning

Regression, classification, trees, SVMs, clustering, PCA — the algorithms that built modern data science, with math and code

Co-Created by Kiran Shirol and Claude

Topics Regression Classification Ensembles Clustering Dimensionality Reduction Evaluation

home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary 10 chapters · 5 sections

Section 1

Foundation — The ML Mindset

What machine learning is and the first algorithm you should master.

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What Is Machine Learning?

Bias-variance trade-off, overfitting, hypothesis spaces, loss functions, and ERM.

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Linear Regression — The Foundation

MSE loss, Normal Equation, gradient descent, Ridge & Lasso regularization.

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Section 2

Classification — Drawing Boundaries

From logistic regression to ensemble methods.

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Logistic Regression & Classification

Sigmoid, cross-entropy, decision boundaries, confusion matrices, ROC-AUC.

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Decision Trees & Random Forests

Gini, entropy, pruning, bagging, XGBoost, LightGBM, and feature importance.

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Section 3

Advanced Models — Beyond Lines and Trees

Kernel methods and probabilistic classifiers.

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Support Vector Machines

Maximum margin, Lagrange multipliers, soft margins, and the kernel trick.

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Naive Bayes & Probabilistic Models

Bayes’ theorem, Gaussian/Multinomial variants, TF-IDF, and Laplace smoothing.

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Section 4

Unsupervised Learning — Finding Structure

Clustering and dimensionality reduction without labels.

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Clustering — K-Means, DBSCAN & Beyond

WCSS, elbow method, silhouette, K-Means++, DBSCAN, and hierarchical clustering.

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Dimensionality Reduction — PCA & t-SNE

Curse of dimensionality, PCA math, scree plots, t-SNE, and UMAP.

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Section 5

The Practitioner’s Toolkit

Evaluation, selection, feature engineering, and the full ML pipeline.

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Model Evaluation & Selection

K-fold CV, GridSearchCV, learning curves, precision-recall trade-offs.

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Feature Engineering & The ML Pipeline

Scaling, encoding, imputation, RFE, scikit-learn Pipeline, and ColumnTransformer.

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Classic Machine Learning

Foundation — The ML Mindset

Classification — Drawing Boundaries

Advanced Models — Beyond Lines and Trees

Unsupervised Learning — Finding Structure

The Practitioner’s Toolkit

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