Build Your Own AI Project Worksheet
AI tip (Accuracy = correct ÷ total × 100): Steps: 1 choose a problem → 2 collect examples → 3 label them → 4 check and correct bad labels (clean the data) → 5 split into training and test sets → 6 train → 7 test with new examples → 8 measure accuracy → 9 improve the data and retest. Accuracy = correct ÷ total × 100. Wrong = total − correct. Expected right = accuracy ÷ 100 × total. Keep some examples apart for testing (for example one fifth); the rest are for training.
NameDateTime takenScore ___ / 40
- 1.🧪 You can plan a simple AI project with… (a) paper and examples (b) nothing at all (c) a secret password (d) only a big robot
- 2.❌ Arjun’s spam spotter was tested on 13 new messages and got 11 right. How many did it get wrong?
- 3.🧪 Why must you test a model on examples it has NOT seen? (a) to see if it learned (b) to make it faster (c) to save paper (d) to make it bigger
- 4.📋 Planning an AI project: which comes first — train the model or split train and test? (a) train the model (b) split train and test
- 5.🎯 Meera’s leaf identifier was tested on 25 new leaf photos and got 25 right. What is its accuracy in percent?
- 6.⚖️ Two handwriting reader models were tested on the same 20 handwritten letters. Model A got 17 right and Model B got 15 right. How many percentage points more accurate is Model A?
- 7.📋 Planning an AI project: which comes first — try unseen examples or collect fruit photos? (a) try unseen examples (b) collect fruit photos
- 8.❌ Tara’s fruit sorter was tested on 39 new fruit photos and got 33 right. How many did it get wrong?
- 9.🎯 Rohan’s recycling sorter is 50% accurate. If it is tested on 80 new waste photos, how many should it get right?
- 10.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) fewer examples (b) more labelled examples (c) a new colour screen (d) a louder speaker
- 11.📋 Planning an AI project: which comes first — choose what to sort or collect fruit photos? (a) choose what to sort (b) collect fruit photos
- 12.📋 Planning an AI project: which comes first — correct bad labels or try unseen examples? (a) correct bad labels (b) try unseen examples
- 13.🎯 Kabir’s bird-song app was tested on 5 new bird songs and got 5 right. What is its accuracy in percent?
- 14.🧪 What is accuracy? (a) total − correct (b) correct ÷ total × 100 (c) correct + total (d) total × 2
- 15.📋 Planning an AI project: which comes first — tag each photo by type or collect fruit photos? (a) tag each photo by type (b) collect fruit photos
- 16.📦 Meera collected 50 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
- 17.❌ Ishaan’s handwriting reader was tested on 47 new handwritten letters and got 40 right. How many did it get wrong?
- 18.📋 Planning an AI project: which comes first — gather many examples or test on new photos? (a) gather many examples (b) test on new photos
- 19.📋 Planning an AI project: which comes first — recheck every label or train the model? (a) recheck every label (b) train the model
- 20.❌ Vihaan’s handwriting reader was tested on 49 new handwritten letters and got 44 right. How many did it get wrong?
- 21.📋 Planning an AI project: which comes first — test on new photos or train the model? (a) test on new photos (b) train the model
- 22.🎯 Meera’s recycling sorter is 70% accurate. If it is tested on 10 new waste photos, how many should it get right?
- 23.🎯 Kabir’s leaf identifier was tested on 4 new leaf photos and got 2 right. What is its accuracy in percent?
- 24.📋 Planning an AI project: which comes first — let it learn patterns or correct bad labels? (a) let it learn patterns (b) correct bad labels
- 25.❌ Ishaan’s spam spotter was tested on 14 new messages and got 13 right. How many did it get wrong?
- 26.📋 Planning an AI project: which comes first — let it learn patterns or split train and test? (a) let it learn patterns (b) split train and test
- 27.🎯 Tara’s bird-song app is 70% accurate. If it is tested on 50 new bird songs, how many should it get right?
- 28.❌ Ishaan’s recycling sorter was tested on 20 new waste photos and got 19 right. How many did it get wrong?
- 29.🎯 Saanvi’s bird-song app was tested on 10 new bird songs and got 9 right. What is its accuracy in percent?
- 30.📦 Kabir collected 60 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
- 31.📋 Planning an AI project: which comes first — gather many examples or split train and test? (a) gather many examples (b) split train and test
- 32.📋 Planning an AI project: which comes first — try unseen examples or calculate percent right? (a) try unseen examples (b) calculate percent right
- 33.📋 Planning an AI project: which comes first — calculate percent right or fix it and test again? (a) calculate percent right (b) fix it and test again
- 34.🎯 Meera’s fruit sorter was tested on 25 new fruit photos and got 21 right. What is its accuracy in percent?
- 35.📋 Planning an AI project: which comes first — train the model or test on new photos? (a) train the model (b) test on new photos
- 36.🎯 Diya’s fruit sorter was tested on 25 new fruit photos and got 25 right. What is its accuracy in percent?
- 37.🎯 Meera’s fruit sorter was tested on 10 new fruit photos and got 9 right. What is its accuracy in percent?
- 38.🎯 Meera’s recycling sorter is 50% accurate. If it is tested on 60 new waste photos, how many should it get right?
- 39.📋 Planning an AI project: which comes first — fix it and test again or calculate percent right? (a) fix it and test again (b) calculate percent right
- 40.📋 Planning an AI project: which comes first — calculate percent right or improve data, retest? (a) calculate percent right (b) improve data, retest
Answer key
- paper and examples
- 2
- to see if it learned
- split train and test
- 100
- 10
- collect fruit photos
- 6
- 40
- more labelled examples
- choose what to sort
- correct bad labels
- 100
- correct ÷ total × 100
- collect fruit photos
- 40
- 7
- gather many examples
- recheck every label
- 5
- train the model
- 7
- 50
- correct bad labels
- 1
- split train and test
- 35
- 1
- 90
- 48
- gather many examples
- try unseen examples
- calculate percent right
- 84
- train the model
- 100
- 90
- 30
- calculate percent right
- calculate percent right
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