Hello Friends in this article i am gone to share Applied Machine Learning in Python Coursera Module 1 Quiz Answers with you..

Applied Machine Learning in Python Quiz Answer

Also visit this link:  Applied Machine Learning in Python Module 4 Quiz Answer

Module 1 Quiz Answer

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 ___________.

• Supervised Learning
• Density Estimation 
• Clustering 
• Unsupervised Learning

Question 2) Select the option that correctly completes the sentence:

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

• Supervised Learning 
• Regression
• Unsupervised Learning 
• Classification

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
• Classification
• Feature Extraction

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  _________.

• Feature Extraction
• Regression
• Classification
• Clustering

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 1

• k=3: Class 2

Question 6) Which of the following is true for the nearest neighbor classifier (Select all that apply):

• A higher value of k leads to a more complex decision boundary
• Partitions observations into k clusters where each observation belongs to the cluster with the nearest mean
• Memorizes the entire training set
• Given a data instance to classify, computes the probability of each possible class using a statistical model of the input features

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

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 amount of labelled data needed for evaluating classifier accuracy
• To reduce the number of features we need to consider as input to the learning algorithm
• To speed up the training process

Question 9) The purpose of setting the random_state parameter in train_test_spIit is: (Select all that apply)

• To avoid predictable splitting of the data
• To make experiments easily reproducible by always using the same partitioning of the data
• To avoid bias in data splitting
• To split the data into similar subsets so that bias is not introduced into the final results

Question 10) 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: (7500, 50)
• y_train: (7500, )
• X_test: (2500, 50)
• y_test: (2500, )