Guided Learning

Build intuition, understand the calculation, check your reasoning, then explore the full model. Completion records activity on your device; it is not a mastery score.

ML Basics

Learn the learning loop before diving into individual models: data, splits, regression, and probabilities.

  1. Clues in, answer out
  2. Practice is not the exam
  3. Fit a straight-line rule
  4. Turn a score into a probability

Classical ML

See how neighbors, trees, margins, and ensembles make different kinds of decisions.

  1. Ask the nearby examples
  2. Split the space with questions
  3. Find a separating margin
  4. Combine many learners

Deep Learning

Build intuition for weighted neurons, shared filters, recurrent state, and self-attention before opening the full labs.

  1. One neuron, one weighted decision
  2. Reuse a small filter across an image
  3. Carry state through a sequence
  4. Let tokens build context from one another

Data & AI Foundations

Understand distributions, projections, evaluation, and explanations so model outputs are easier to reason about.

  1. Describe the data before modeling
  2. Project data onto informative directions
  3. Measure the right kind of error
  4. Explain a prediction without inventing causality