Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 551 · 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.Which of these is good for training a model? (a) copies of one photo (b) clear, sharp examples (c) labels added at random (d) very few examples
2. 2.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) checking labels twice (d) blurry, unclear photos
3. 3.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
4. 4.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) blurry, unclear photos (d) testing on training data
5. 5.True or false: “examples of every type” is bad for training a model. (a) True (b) False
6. 6.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
7. 7.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) many varied examples (d) checking labels twice
8. 8.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
9. 9.🔍 Which is the odd one out? (a) very few examples (b) checking labels twice (c) blurry, unclear photos (d) labels added at random
10. 10.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
11. 11.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) many varied examples (d) examples of every type
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
14. 14.True or false: “very few examples” is good for training a model. (a) False (b) True
15. 15.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (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? “Science project groups are on the board” (a) spam (b) not spam
17. 17.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
18. 18.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
19. 19.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
21. 21.Which of these is bad for training a model? (a) new data for testing (b) only one kind of example (c) many varied examples (d) clear, sharp examples
22. 22.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) many varied examples (d) testing on training data
23. 23.True or false: “labels added at random” is good for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
25. 25.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will be perfect (d) it will turn yellow
26. 26.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
27. 27.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
28. 28.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) only one kind of example (d) blurry, unclear photos
29. 29.🔍 Which is the odd one out? (a) only one kind of example (b) wrong labels (c) very few examples (d) clear, sharp examples
30. 30.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) many labelled photos (c) one single photo (d) a list of rules only
31. 31.🏷️ 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
32. 32.🏷️ 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
33. 33.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
34. 34.🔍 Which is the odd one out? (a) only one kind of example (b) wrong labels (c) examples of every type (d) very few examples
35. 35.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
36. 36.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
38. 38.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
39. 39.🏷️ 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
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam

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