Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 137 · 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.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
2. 2.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
3. 3.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) very few examples (d) many varied examples
4. 4.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) photos in many lights (d) correct labels
5. 5.Which of these is good for training a model? (a) checking labels twice (b) labels added at random (c) blurry, unclear photos (d) copies of one photo
6. 6.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
7. 7.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) wrong labels (d) examples of every type
8. 8.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) new data for testing (d) labels added at random
9. 9.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) many labelled photos (c) a list of rules only (d) one single photo
11. 11.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
12. 12.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
13. 13.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
14. 14.True or false: “copies of one photo” is good for training a model. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) many varied examples (d) wrong labels
16. 16.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
17. 17.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
18. 18.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) labels added at random
19. 19.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) clear, sharp examples (d) blurry, unclear photos
20. 20.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) copies of one photo (d) blurry, unclear photos
21. 21.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) very few examples (d) examples of every type
23. 23.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) blurry, unclear photos (d) examples of every type
24. 24.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
25. 25.🏷️ 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
26. 26.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) clear, sharp examples (d) copies of one photo
27. 27.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) clear, sharp examples (d) only one kind of example
28. 28.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) new data for testing (d) testing on training data
29. 29.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
31. 31.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
32. 32.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
33. 33.True or false: “very few examples” is good for training a model. (a) False (b) True
34. 34.True or false: “many varied examples” is good for training a model. (a) False (b) True
35. 35.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
36. 36.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
38. 38.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
39. 39.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
40. 40.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price

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