Week 2 of 6 · 2.5-3 hours
How Data Teaches a Model
See how examples, labels, and features become the data a model learns from — and why clean, balanced data matters.
Big question: How does data teach a model, and what makes data good enough to learn from?
By the end of this week
- Identify the examples, labels, features, and categories in a dataset.
- Explain how patterns link features to categories.
- Describe why models are trained on one set of data and tested on another, and define generalization and accuracy.
- Find and repair duplicates, incorrect labels, and unbalanced categories.
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- Examples, Labels, and FeaturesOpen up a dataset and meet its building blocks: each example, the label that says which category it belongs to, and the features that describe it.
- Training Data Versus Testing DataSee why a model is taught on one set of examples and checked on another, and how testing on unseen examples measures whether it really learned the pattern.
- Repair the DatasetPlay data detective: hunt down duplicates, incorrect labels, and unbalanced categories, then fix them so a model can learn a fair, accurate pattern.