Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 548 · 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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
2. 2.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
3. 3.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
4. 4.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) only one kind of example (d) wrong labels
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
8. 8.True or false: “many varied examples” is good for training a model. (a) True (b) False
9. 9.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo
11. 11.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) examples of every type (d) checking labels twice
12. 12.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
13. 13.True or false: “very few examples” is good for training a model. (a) False (b) True
14. 14.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) many varied examples (d) wrong labels
15. 15.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) clean screens (d) charge faster
16. 16.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) testing on training data (d) many varied examples
17. 17.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) blurry, unclear photos (d) clear, sharp examples
18. 18.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
19. 19.True or false: “new data for testing” is bad for training a model. (a) True (b) False
20. 20.🥭 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 may get it wrong (d) it will be perfect
21. 21.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
23. 23.🔍 Which is the odd one out? (a) new data for testing (b) labels added at random (c) examples of every type (d) photos in many lights
24. 24.🏷️ 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
25. 25.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
26. 26.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) copies of one photo (d) examples of every type
27. 27.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) correct labels (d) very few examples
28. 28.True or false: “testing on training data” is bad for training a model. (a) False (b) True
29. 29.True or false: “many varied examples” is bad for training a model. (a) False (b) True
30. 30.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) new data for testing (d) testing on training data
31. 31.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
32. 32.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
33. 33.Which of these is good for training a model? (a) new data for testing (b) blurry, unclear photos (c) only one kind of example (d) wrong labels
34. 34.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) clear, sharp examples (d) copies of one photo
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
36. 36.🏷️ 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
37. 37.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
38. 38.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
39. 39.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
40. 40.True or false: “examples of every type” is bad for training a model. (a) False (b) True

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