A startup needs a spam filter, and you are the training data. Label 10 emails — spam or inbox. Your model learns only from your labels: no second chances, no corrections. Then it goes live on real email... and the real world has surprises for it. How accurate will YOUR model be?
This is exactly how supervised learning works: humans label examples, the model learns the mapping, and every labeling mistake becomes a model mistake. Module 02 explains the theory — here you'll feel it.
No feedback yet — the model trusts you completely. Every label you give becomes its truth.
Your model never saw the emails — it only saw your labels. Every mistake you made during training became a rule it faithfully learned. In real ML, labeling quality is destiny.
The model learned "free = spam" as a pattern, so HR's free-lunch email got flagged. It matched the feature, not the meaning. That's a false positive — and the seed of overfitting.
The polite phishing email sailed through because nothing like it existed in your 10 examples. Models fail quietly on inputs unlike anything they trained on — which is why coverage matters more than volume.