Machine Learning & AI — Beginner

Machine Learning & AI — Foundations: 13 topics and 28 interactive cards with runnable examples and quizzes.

Topics

1. What Is Machine Learning? — Rules vs Learning · The Three Families
2. Data — Features, Labels & Splitting — Features and Labels · Train / Validation / Test
3. Linear Regression — Fitting a Line — A Line Through the Data · Measuring the Error
4. How Models Learn — Gradient Descent — Walking Downhill · Training a Line by Hand
5. Classification — Predicting Categories — From Numbers to Yes/No · A Tiny Classifier
6. Decision Trees — A Flowchart of Questions · Finding the Best Split
7. Evaluating a Model Honestly — Accuracy and Its Limits · Precision, Recall & F1
8. Overfitting & the Bias-Variance Tradeoff — Memorising vs Learning · The Bias-Variance Tradeoff
9. The ML Workflow — The End-to-End Pipeline
10. Mini-Project — A Spam Classifier — Learn From Examples · The Real-World Version
11. Clustering — Finding Groups Without Labels — Learning Without an Answer Key · Real k-means with scikit-learn · Seeing the Clusters
12. Your First Real scikit-learn Model — The Iris Dataset · Split, Fit, Score · What Did It Learn?
13. Words as Numbers — A Taste of NLP — Turning Sentences into Vectors · A Tiny Sentiment Classifier · Now You Try!