Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 272 · 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 library book is due on Monday” should be labelled “spam”. (a) True (b) False
2. 2.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
3. 3.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
4. 4.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
5. 5.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
6. 6.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) copies of one photo (d) clear, sharp examples
7. 7.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
8. 8.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will turn yellow (d) it will shut down
9. 9.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
10. 10.True or false: “testing on training data” is good for training a model. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) photos in many lights (d) correct labels
12. 12.True or false: “examples of every type” is good for training a model. (a) False (b) True
13. 13.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
14. 14.🏷️ 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
15. 15.🔍 Which is the odd one out? (a) wrong labels (b) very few examples (c) examples of every type (d) testing on training data
16. 16.🏷️ 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
17. 17.🏷️ 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
18. 18.Which of these is good for training a model? (a) only one kind of example (b) correct labels (c) blurry, unclear photos (d) wrong labels
19. 19.🔍 Which is the odd one out? (a) wrong labels (b) photos in many lights (c) clear, sharp examples (d) examples of every type
20. 20.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
21. 21.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
22. 22.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
23. 23.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) new data for testing (d) checking labels twice
25. 25.True or false: “labels added at random” is good for training a model. (a) False (b) True
26. 26.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
27. 27.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) copies of one photo (d) correct labels
28. 28.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) photos in many lights (d) testing on training data
29. 29.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) clear, sharp examples (d) blurry, unclear photos
30. 30.🔍 Which is the odd one out? (a) testing on training data (b) clear, sharp examples (c) photos in many lights (d) new data for testing
31. 31.Which of these is bad for training a model? (a) examples of every type (b) blurry, unclear photos (c) clear, sharp examples (d) correct labels
32. 32.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) only one kind of example (d) clear, sharp examples
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) wrong labels (d) new data for testing
34. 34.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
35. 35.🏷️ 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
36. 36.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
37. 37.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
38. 38.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) correct labels (d) copies of one photo
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
40. 40.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only

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