How training data shapes AI behavior in AI for Everyone - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show how AI learns from training data.

AI for Everyone

model.fit([1], labels, epochs=5)

Drag options to blanks, or click blank then click option'

Atest_data

Btraining_data

Cvalidation_data

Drandom_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using test or validation data instead of training data.

The model learns patterns from the training_data. Using test or validation data here would not train the model.

2fill in blank

medium

Complete the code to split data into training and testing sets.

AI for Everyone

train_data, test_data = data.[1](test_size=0.2)

Drag options to blanks, or click blank then click option'

Atrain_test_split

Bsplit

Cmerge

Dshuffle

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'split' or 'shuffle' which are not the correct function names.

The function train_test_split divides data into training and testing parts, which is essential for evaluating AI behavior.

3fill in blank

hard

Fix the error in the code that trains a model with biased data.

AI for Everyone

if data.is_biased():
    model.[1](data)

Drag options to blanks, or click blank then click option'

Apredict

Btrain

Cevaluate

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a valid method.

Using 'predict' which is for making predictions.

The method fit is used to train the model on data. Using 'train' or 'predict' here is incorrect syntax or purpose.

4fill in blank

hard

Fill both blanks to create a dictionary showing word lengths only for words longer than 3 letters.

AI for Everyone

{word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B==

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' or '<' which do not filter words longer than 3 letters.

We want the length of each word as the value, so len(word) is correct. The condition len(word) > 3 filters words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of words and their lengths where length is greater than 4.

AI for Everyone

filtered = { [1]: [2] for [3] in words if len([3]) > 4 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names causing errors.

Using incorrect value expressions.

The dictionary keys are words (word), values are their lengths (len(word)), and the loop variable is word. Using 'item' would cause a mismatch.

Practice

1. Why is training data important for AI behavior?

easy

A. Because training data controls the hardware AI runs on

B. Because training data is used to write AI's code manually

C. Because training data is only used after AI is fully built

D. Because AI learns patterns from training data to make decisions

Solution

1. Step 1: Understand AI learning process

   AI learns by finding patterns in examples called training data.

2. Step 2: Connect training data to AI decisions

   Good training data helps AI make better decisions by showing it what to expect.

3. Final Answer:

   Because AI learns patterns from training data to make decisions -> Option D

4. Quick Check:

   Training data shapes AI behavior = C [OK]

Hint: AI learns from examples, not from code or hardware [OK]

Common Mistakes:

• Thinking AI code is written from training data
• Confusing training data with hardware
• Believing training data is used after AI is built

2. Which of these is the correct way to describe training data's role?

easy

A. Training data is the set of examples AI uses to learn

B. Training data is the AI's final output after learning

C. Training data is the software that runs AI models

D. Training data is the hardware AI uses to process information

Solution

1. Step 1: Define training data

   Training data is the collection of examples given to AI to learn from.

2. Step 2: Eliminate incorrect options

   Training data is not output, software, or hardware but input examples.

3. Final Answer:

   Training data is the set of examples AI uses to learn -> Option A

4. Quick Check:

   Training data = learning examples [OK]

Hint: Training data = examples AI learns from, not output or hardware [OK]

Common Mistakes:

• Confusing training data with AI output
• Mixing training data with software or hardware
• Thinking training data runs the AI

3. Consider this code snippet simulating AI training data influence:

training_data = ['cat', 'dog', 'cat', 'bird']
model_behavior = {animal: training_data.count(animal) for animal in set(training_data)}
print(model_behavior)

What is the output?

medium

A. {'cat': 1, 'dog': 1, 'bird': 1}

B. {'cat': 2, 'dog': 1, 'bird': 1}

C. {'cat': 3, 'dog': 1, 'bird': 1}

D. Error: count method not found

Solution

1. Step 1: Understand the code logic

   The code counts how many times each unique animal appears in training_data.

2. Step 2: Count occurrences

   'cat' appears twice, 'dog' once, 'bird' once, so the dictionary reflects these counts.

3. Final Answer:

   {'cat': 2, 'dog': 1, 'bird': 1} -> Option B

4. Quick Check:

   Count of each animal in list = {'cat': 2, 'dog': 1, 'bird': 1} [OK]

Hint: Count items in list using count() for each unique element [OK]

Common Mistakes:

• Assuming count counts total list length
• Confusing set with list order
• Thinking count method causes error

4. This code tries to count training data items but has a bug:

training_data = ['apple', 'banana', 'apple']
model_behavior = {animal: training_data.count for animal in set(training_data)}
print(model_behavior)

What is the error and how to fix it?

medium

A. Error: set() cannot be used on lists

B. Error: print() cannot display dictionaries

C. Error: count is a method, should be called with parentheses like count()

D. No error, code runs fine

Solution

1. Step 1: Identify the bug in code

   The code uses training_data.count without parentheses, so it refers to the method itself, not calling it.

2. Step 2: Fix the method call

   Adding parentheses and argument fixes it: training_data.count(animal)

3. Final Answer:

   Error: count is a method, should be called with parentheses like count() -> Option C

4. Quick Check:

   Method call missing parentheses = B [OK]

Hint: Remember to call methods with () to execute them [OK]

Common Mistakes:

• Thinking set() can't be used on lists
• Believing print can't show dictionaries
• Ignoring missing parentheses on method calls

5. An AI model trained only on photos of cats and dogs struggles to recognize birds. What is the best way to improve its behavior?

hard

A. Add more bird photos to the training data

B. Remove all cat and dog photos from training data

C. Change the AI model architecture without changing data

D. Train the AI only on bird photos

Solution

1. Step 1: Identify the problem with training data

   The model lacks examples of birds, so it cannot learn to recognize them well.

2. Step 2: Improve training data quality

   Adding bird photos gives the AI examples to learn bird features, improving recognition.

3. Final Answer:

   Add more bird photos to the training data -> Option A

4. Quick Check:

   More diverse training data = better AI behavior [OK]

Hint: Add missing examples to training data to improve AI [OK]

Common Mistakes:

• Removing existing data reduces AI knowledge
• Changing model without data won't fix missing knowledge
• Training only on birds loses knowledge of cats and dogs