Machine Learning Foundations
This course constitutes the foundational core of machine learning. It builds on Introduction to AI (the concepts) and Math Foundations (the tools), translating them into working models. By the end you will understand the modeling workflow, fit regression and classification models, train naive Bayes, decision trees, k-means clustering, and PCA, and — most importantly — evaluate a model rigorously, so that you can distinguish a genuine result from a misleading one.
Every module is hands-on and runs entirely in your browser: you will adjust a regression line to reduce the error, observe k-means centroids converge, grow a decision tree split by split, and observe a model overfit. Each module also presents the corresponding scikit-learn code for later recognition. Each module concludes with a short mastery check; passing it marks the module complete.
Start here
Module 1The Modeling Workflow
Data → features → model → loss → train → evaluate, and the train/test split that ensures honest evaluation. Supervised vs. unsupervised; regression vs. classification. Activity: run an example through a live pipeline, then classify real tasks by type.
Supervised learning
Module 2Regression — Fitting a Line
Predicting a number. Residuals, squared error, and the line of best fit. Activity: drag a line over real points and watch the error fall — then let it auto-fit. AI anchor: price and demand prediction.
Module 3Classification — Drawing Boundaries
Predicting a category. k-nearest-neighbors, decision boundaries, and probability outputs. Activity: drop a new point and watch its neighbors vote, with an adjustable k. AI anchor: spam, medical, and image classifiers.
Module 4Naive Bayes
Turning Bayes’ rule into a classifier. The independence assumption and why it works anyway. Activity: a live spam filter — toggle words and watch the spam probability update. AI anchor: real spam and sentiment filters.
Module 5Decision Trees
Binary tests that partition the data; impurity, greedy splits, and depth. Activity: grow a tree with a depth slider and observe it partition the plane — and begin to overfit. AI anchor: random forests and gradient boosting.
Unsupervised learning
Module 6Clustering — k-means
Finding groups with no labels at all. Centroids, the assign-and-update loop, and choosing k. Activity: step through k-means and observe the clusters form; inertia decreases each round. AI anchor: customer segmentation.
Module 7Dimensionality Reduction — PCA
Too many features; find the directions that carry the most signal. Variance, principal components, and projection. Activity: rotate a projection line and watch retained variance peak on the first component. AI anchor: visualizing embeddings.
Project
Module 8 · ProjectHonest Evaluation — Overfitting, Cross-Validation & Bias–Variance
The methodology that distinguishes rigorous machine learning from self-deception. Observe a model overfit as its complexity increases — training error decreases while test error increases — and learn how cross-validation and the bias–variance trade-off support rigorous evaluation. Integrates every earlier module.
Capstone
CapstoneBuild a Concept Manipulative
Put it all together: build a single-page interactive that teaches one ML Foundations concept, then submit it for grading and your certificate.