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ML

Machine Learning

Supervised setup, linear models, trees, evaluation. Later headings open after the one before. Set up supervised learning, fit linear models and trees, evaluate honestly, and regularise — as a first ML course does.

  • 6 headings
  • Ladder
  • Test out a heading you have
Start Machine Learning free

Free to start. Sign in with Google or email.

A class on this path

Supervised setup · First class

Live tutor
Materials for this classReadingPodcastLecture

Pick where you want to start

Each heading is its own course. Later ones open when you finish — or skip — the one before. A later course does not reteach earlier bands.

  1. Supervised setup

    Start here

    Define features, label, train/val/test, and the loss you are minimising on a concrete table of data.

  2. Linear models

    After Supervised setup — test out to unlock

    Fit linear regression and logistic regression, and interpret a coefficient with the right caveats.

  3. Evaluation

    After Linear models — test out to unlock

    Choose accuracy vs precision/recall vs AUC for a problem, and catch leakage or a bad split.

  4. Trees and ensembles

    After Evaluation — test out to unlock

    Grow a decision tree by hand on a tiny set, and say what bagging or boosting is changing.

  5. Unsupervised

    After Trees and ensembles — test out to unlock

    Run k-means or PCA on a small example and say what the output is (and is not) claiming.

  6. Regularization and bias

    After Unsupervised — test out to unlock

    Use L2 / early stopping / depth limits, and diagnose bias vs variance from a learning curve.

What this is for

  1. 01

    Define features, label, train/val/test, and the loss you are minimising on a concrete table of data.

  2. 02

    Fit linear regression and logistic regression, and interpret a coefficient with the right caveats.

  3. 03

    Choose accuracy vs precision/recall vs AUC for a problem, and catch leakage or a bad split.

Why a path, not a playlist

Supervised setup, linear models, trees, evaluation. Later headings open after the one before.

Each heading is its own course with a tutor. Later headings open when you finish — or test out of — the one before.

After you sign in

  • You land on this map — every heading is already there.
  • Take the open class, or test out of a later heading to unlock it.
  • Each heading is one shared course — same syllabus and first class for everyone. Later classes continue on your copy.

ML

Start Machine Learning free

Free to start. Sign in with Google or email.