Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 195 · 40 questions

AI tip (Show, label, learn): Machine learning learns patterns from many labelled examples — a label is the right answer tag on each example. Good for training: many varied examples, correct labels, every type the model will meet, and separate new data for testing. Bad for training: very few examples, wrong or random labels, only one kind of example, blurry examples, testing on the training data.

1. 1.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
2. 2.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) very few examples (d) clear, sharp examples
3. 3.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) labels added at random (d) wrong labels
4. 4.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
5. 5.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) wrong labels (c) checking labels twice (d) many varied examples
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
8. 8.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
9. 9.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) copies of one photo (d) examples of every type
10. 10.Which of these is bad for training a model? (a) testing on training data (b) correct labels (c) many varied examples (d) examples of every type
11. 11.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) very few examples (d) checking labels twice
12. 12.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
13. 13.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
14. 14.True or false: “new data for testing” is good for training a model. (a) False (b) True
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
16. 16.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
17. 17.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
18. 18.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
19. 19.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
21. 21.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
22. 22.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
24. 24.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) clear, sharp examples (d) many varied examples
25. 25.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) wrong labels (d) new data for testing
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
27. 27.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) blurry, unclear photos (d) labels added at random
28. 28.Which of these is good for training a model? (a) labels added at random (b) checking labels twice (c) only one kind of example (d) testing on training data
29. 29.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) clear, sharp examples (d) labels added at random
30. 30.🔍 Which is the odd one out? (a) photos in many lights (b) copies of one photo (c) testing on training data (d) only one kind of example
31. 31.Which of these is bad for training a model? (a) checking labels twice (b) blurry, unclear photos (c) new data for testing (d) correct labels
32. 32.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
33. 33.Which of these is bad for training a model? (a) only one kind of example (b) new data for testing (c) photos in many lights (d) examples of every type
34. 34.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
35. 35.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) examples of every type (d) testing on training data
36. 36.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
37. 37.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
39. 39.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam

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