Definition
You've learned every step. Can you build, test and improve a model on your own?
This project brings together the whole learning process: choosing a task, choosing labelled examples, training a model, testing it on new examples, recording its accuracy, finding its mistakes and improving it, with no programming.
In simple words
You will use the cat-and-dog trainer from start to finish. Pick a small set of examples, test the model, write down the score, find out why it went wrong, add better examples and test again. Keep a record of every test, just as real AI teams do.
Why it matters
Doing the whole cycle yourself makes every idea in this module real: data, labels, features, training, testing, accuracy, mistakes and bias. It is the same cycle used to build models for hospitals, banks and phones.
How it works
Project checklist
- Plan: write the task in one sentence and name the two features.
- Collect: choose six training examples, three of each kind.
- Train and test: test on all nine new animals and record the accuracy.
- Investigate: list the animals it got wrong. What do they have in common?
- Improve: add examples that cover those cases, then test again.
- Report: write two sentences on what changed and why.
Try it yourself
Guided project in the browser
Work through the checklist above. Every test you run is recorded below the buttons.
Your test record
Copy this table on paper and fill it in as you work.
- Test 1: examples used, accuracy, animals it got wrong.
- Change made: which examples you added and why.
- Test 2: accuracy, animals it still got wrong.
- Conclusion: one sentence on what the model needed.
Common mistakes
- Adding examples without first studying the mistakes.
- Changing many things at once, so you can't tell what helped.
- Forgetting to record each test.
Check yourself
Summary
- A full project: plan, collect, train, test, investigate, improve and report.
- Records of each test show what helped.
- Better, more varied examples are usually the best fix.
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