Applied Machine LearningCourse

AI Foundations / 7 weeks

Applied Machine Learning

Teaches practitioners how to frame a prediction problem, pick a model family, evaluate honestly, and decide when a model is good enough to ship.

Learning path

Six modules with practical application.

  1. Turning a business question into a prediction problem, defining the target, unit of analysis, and decision the model will support
  2. Building training, validation, and test sets that reflect reality, with labels that are correct, complete, and faithful to the task
  3. Linear models, trees, ensembles, neural networks, and which family fits which kind of problem, dataset size, and interpretability need
  4. Picking the right metric for the decision, building a credible baseline, and reading precision, recall, calibration, and segment performance honestly
  5. Inspecting where the model fails, why it fails, and what to fix in data, features, or model choice before another training round
  6. Deciding when a model is good enough, picking the threshold, defining the human review path, and what to monitor after launch

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