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Applied Machine Learning in Python

Week 1

Module 1 Quiz

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• • Reading: ReadingCourse Syllabus

    10 min
  • Video: LectureIntroduction

    11 min
  • Reading: ReadingHelp us learn more about you!

    10 min
  • Video: LectureKey Concepts in Machine Learning

    13 min
  • Video: LecturePython Tools for Machine Learning

    4 min
  • Reading: ReadingNotice for Auditing Learners: Assignment Submission

    10 min
  • Notebook: Module 1 Notebook

    1h
  • Video: LectureAn Example Machine Learning Problem

    12 min
  • Video: LectureExamining the Data

    9 min
  • Video: LectureK-Nearest Neighbors Classification

    20 min
  • Reading: ReadingZachary Lipton: The Foundations of Algorithmic Bias (optional)

    30 min
  • Quiz: Module 1 Quiz

    10 questions

QUIZ • 20 MIN

Module 1 Quiz

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DUEJan 13, 1:29 PM IST

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Module 1 Quiz

Graded Quiz • 20 min

Due Jan 13, 1:29 PM Module 1 Quiz

TOTAL POINTS 10

1.Question 1

Select the option that correctly completes the sentence:

Training a model using labeled data and using this model to predict the labels for new data is known as ____________.

Density Estimation

Unsupervised Learning

Supervised Learning

Clustering

1 point

2.Question 2

Select the option that correctly completes the sentence:

Modeling the features of an unlabeled dataset to find hidden structure is known as ____________.

Regression

Supervised Learning

Classification

Unsupervised Learning

1 point

3.Question 3

Select the option that correctly completes the sentence:

Training a model using categorically labelled data to predict labels for new data is known as __________.

Regression

Clustering

Feature Extraction

Classification

1 point

4.Question 4

Select the option that correctly completes the sentence:

Training a model using labelled data where the labels are continuous quantities to predict labels for new data is known as __________.

Classification

Feature Extraction

Clustering

Regression

1 point

5.Question 5

Using the data for classes 0, 1, and 2 plotted below, what class would a KNeighborsClassifier classify the new point as for k = 1 and k = 3?

• k=1: Class 2
• k=3: Class 1

• k=1: Class 0
• k=3: Class 1

• k=1: Class 1
• k=3: Class 0

• k=1: Class 1
• k=3: Class 2

• k=1: Class 0
• k=3: Class 2

1 point

6.Question 6

Which of the following is true for the nearest neighbor classifier (Select all that apply):

Partitions observations into k clusters where each observation belongs to the cluster with the nearest mean

Memorizes the entire training set

A higher value of k leads to a more complex decision boundary

Given a data instance to classify, computes the probability of each possible class using a statistical model of the input features

1 point

7.Question 7

Why is it important to examine your dataset as a first step in applying machine learning? (Select all that apply):

See what type of cleaning or preprocessing still needs to be done

You might notice missing data

Gain insight on what machine learning model might be appropriate, if any

Get a sense for how difficult the problem might be

It is not important

1 point

8.Question 8

The key purpose of splitting the dataset into training and test sets is:

To estimate how well the learned model will generalize to new data

To reduce the number of features we need to consider as input to the learning algorithm

To reduce the amount of labelled data needed for evaluating classifier accuracy

To speed up the training process

1 point

9.Question 9

The purpose of setting the random_state parameter in train_test_split is: (Select all that apply)

To make experiments easily reproducible by always using the same partitioning of the data

To split the data into similar subsets so that bias is not introduced into the final results

To avoid bias in data splitting

To avoid predictable splitting of the data

1 point

10.Question 10

Given a dataset with 10,000 observations and 50 features plus one label, what would be the dimensions of X_train, y_train, X_test, and y_test? Assume a train/test split of 75%/25%.

• X_train: (2500, 50)
• y_train: (2500, )
• X_test: (7500, 50)
• y_test: (7500, )

• X_train: (7500, 50)
• y_train: (7500, )
• X_test: (2500, 50)
• y_test: (2500, )

• X_train: (10000, 50)
• y_train: (10000, )
• X_test: (10000, 50)
• y_test: (10000, )

• X_train: (2500, )
• y_train: (2500, 50)
• X_test: (7500, )
• y_test: (7500, 50)

• X_train: (10000, 28)
• y_train: (10000, )
• X_test: (10000, 12)
• y_test: (10000, )

1 point

I, Adesh Singh, understand that submitting work that isn’t my own may result in permanent failure of this course or deactivation of my Coursera account.