Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 520 · 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: correct labels? (a) bad for training (b) good for training
2. 2.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
4. 4.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) clear, sharp examples (d) copies of one photo
6. 6.🏷️ 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
7. 7.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
8. 8.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
9. 9.True or false: “many varied examples” is good for training a model. (a) True (b) False
10. 10.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
11. 11.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
12. 12.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) one single photo (d) a song about dogs
13. 13.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
14. 14.True or false: “photos in many lights” is good for training a model. (a) True (b) False
15. 15.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
16. 16.Which of these is bad for training a model? (a) photos in many lights (b) very few examples (c) checking labels twice (d) new data for testing
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) testing on training data (d) clear, sharp examples
19. 19.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
20. 20.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
21. 21.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
22. 22.True or false: “copies of one photo” is good for training a model. (a) False (b) True
23. 23.🏷️ 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
24. 24.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) wrong labels (d) checking labels twice
25. 25.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) photos in many lights (d) examples of every type
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) testing on training data (d) blurry, unclear photos
28. 28.True or false: “very few examples” is good for training a model. (a) False (b) True
29. 29.Which of these is bad for training a model? (a) photos in many lights (b) checking labels twice (c) clear, sharp examples (d) labels added at random
30. 30.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) change colour (d) forget everything
31. 31.True or false: “wrong labels” is bad for training a model. (a) False (b) True
32. 32.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) very few examples (d) testing on training data
33. 33.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: “very few examples” is bad for training a model. (a) False (b) True
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) labels added at random (d) checking labels twice
36. 36.🏷️ 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
37. 37.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
38. 38.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
39. 39.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
40. 40.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) wrong labels (d) photos in many lights

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students