Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 562 · 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 “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
2. 2.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
3. 3.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
4. 4.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) blurry, unclear photos (d) many varied examples
5. 5.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) photos in many lights (d) labels added at random
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
7. 7.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) new data for testing (d) testing on training data
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
9. 9.Which of these is bad for training a model? (a) testing on training data (b) examples of every type (c) many varied examples (d) photos in many lights
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
11. 11.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
12. 12.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
14. 14.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) blurry, unclear photos (d) very few examples
15. 15.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) examples of every type (d) very few examples
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
17. 17.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
18. 18.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) correct labels (d) blurry, unclear photos
19. 19.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
20. 20.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) clear, sharp examples (d) copies of one photo
21. 21.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
22. 22.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) forget everything (d) change colour
23. 23.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
25. 25.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) a song about dogs (d) one single photo
26. 26.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) correct labels (d) checking labels twice
27. 27.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) copies of one photo (d) examples of every type
28. 28.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
29. 29.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) very few examples (d) labels added at random
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
31. 31.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) the font size (d) a sticker on a laptop
32. 32.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
33. 33.True or false: “examples of every type” is bad for training a model. (a) True (b) False
34. 34.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
36. 36.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) examples of every type (d) blurry, unclear photos
37. 37.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) correct labels (d) very few examples
38. 38.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) testing on training data (d) checking labels twice
39. 39.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) very few examples (d) photos in many lights
40. 40.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True

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