Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 522 · 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.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
2. 2.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
3. 3.🐶 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
4. 4.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
5. 5.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) testing on training data (d) labels added at random
6. 6.🔍 Which is the odd one out? (a) very few examples (b) examples of every type (c) blurry, unclear photos (d) wrong labels
7. 7.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
9. 9.True or false: “labels added at random” is bad for training a model. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) copies of one photo (d) new data for testing
11. 11.🔍 Which is the odd one out? (a) blurry, unclear photos (b) photos in many lights (c) testing on training data (d) copies of one photo
12. 12.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
13. 13.🏷️ 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
14. 14.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) only one kind of example (d) blurry, unclear photos
16. 16.🔍 Which is the odd one out? (a) correct labels (b) testing on training data (c) checking labels twice (d) new data for testing
17. 17.True or false: “photos in many lights” is good for training a model. (a) False (b) True
18. 18.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
19. 19.True or false: “new data for testing” is bad for training a model. (a) True (b) False
20. 20.True or false: “very few examples” is good for training a model. (a) False (b) True
21. 21.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
22. 22.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
24. 24.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) correct labels (d) wrong labels
25. 25.🏷️ 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
26. 26.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
27. 27.🏷️ 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
28. 28.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
29. 29.🏷️ 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
30. 30.🏷️ 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
31. 31.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
32. 32.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
33. 33.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) very few examples (d) many varied examples
34. 34.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) many varied examples (d) examples of every type
35. 35.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) clear, sharp examples (d) photos in many lights
36. 36.True or false: “only one kind of example” is good for training a model. (a) False (b) True
37. 37.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
38. 38.🏷️ 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
39. 39.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
40. 40.🏷️ 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

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