ML vs traditional programming in ML Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show how traditional programming uses rules to get output.

ML Python

def traditional_programming(input_data):
    # Use rules to get output
    if input_data == 'sunny':
        return [1]
    else:
        return 'unknown'

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

A'learn model'

B'go outside'

C'train data'

D'predict output'

Attempts:

3 left

2fill in blank

medium

Complete the code to show how machine learning uses data to learn a model.

ML Python

def machine_learning(data):
    # Learn a model from data
    model = [1](data)
    return model

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

Atrain_model

Brule_engine

Cfixed_rules

Dmanual_code

Attempts:

3 left

3fill in blank

hard

Fix the error in the code that predicts output using a learned model.

ML Python

def predict(model, input_data):
    # Predict output using model
    output = model.[1](input_data)
    return output

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

Afit

Blearn

Ctrain

Dpredict

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that shows ML learns from data length and compares it.

ML Python

results = {item: len(item) [1] 3 for item in data if len(item) [2] 2}

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

A>

B<

C==

D!=

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters and transforms data based on value comparisons.

ML Python

filtered = [1]: [2] for [1], [2] in dataset.items() if [2] [3] 10}

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

Akey

Bvalue

C>

D<

Attempts:

3 left