Learn Machine Learning

Teach models from data, measure what they learn, and monitor what happens next.

Start learning 6 lessons · about 1 hours · free
About this track

Build a practical foundation in machine learning: problem framing, features and data preparation, training and generalization, evaluation, unsupervised methods, and responsible deployment.

Before you start
  • No prior machine learning experience needed.
  • Basic Python and math are helpful but not required.
  • A modern browser with JavaScript enabled.
  1. Unit 1 · 0/2 lessons

    ML foundations

    Frame a learning task and prepare trustworthy model inputs. Badge: Data ready

    1. 0x001Understand machine learning problemsConnect examples, features, labels, and objectives to the kind of problem you want to solve. 12 min 12 min
    2. 0x102Prepare data and featuresInspect examples, handle missing values, and transform inputs without leaking information. 12 min 12 min
  2. Unit 2 · 0/2 lessons

    Training and evaluation

    Fit models, check generalization, and choose useful metrics. Badge: Model evaluator

    1. 0x203Train models and generalize to new dataFit a model, recognize overfitting, and use validation data for model choices. 12 min 12 min
    2. 0x304Choose useful evaluation metricsMatch metrics to the cost of errors and the prediction task. 12 min 12 min
  3. Unit 3 · 0/2 lessons

    Patterns and deployment

    Explore unsupervised methods and responsible production monitoring. Badge: Responsible learner

    1. 0x405Find structure without labelsExplore clustering and dimensionality reduction while interpreting results cautiously. 12 min 12 min
    2. 0x506Deploy, monitor, and govern ML systemsPlan for changing data, uneven impacts, privacy, and human review after launch. 12 min 12 min
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