Machine Learning
How do observations become a model—and how much should we trust it? Start with probability, learn what a prediction estimates, then fit a model and evaluate it on new observations.
Learning modules
Section titled “Learning modules”| Sequence | What you will learn | Open |
|---|---|---|
| Probability foundations | Describe randomness, averages, spread, and conditioning. | Lessons · Practice |
| Module 2 · Learning from data | Fit a prediction, evaluate new observations, and separate bias, variance, and noise. | Lessons · Worked questions |
| Module 3 · Linear regression | Work with matrices, fit a line, measure its errors, and judge coefficient uncertainty. | One-page guide + practice |
| Module 4 · Flexible regression | Add categories, interactions, and curves; compare models on training and test data. | One-page guide + practice |
Where to begin
Section titled “Where to begin”Start with probability for randomness and conditioning, or learning from data for fitting and evaluating predictions.
If the symbols are the obstacle, use worked notation questions. If you want a slower mathematical foundation, linear algebra and calculus are available under Math.
Try each lesson’s question before opening the worked answer. The goal is to be able to reconstruct the calculation and recognize it when a new problem changes the notation.