Coursera Answers

Applied Machine Learning in Python Module 1 Quiz Answer

Applied Machine Learning in Python Module 1 Quiz Answer

Applied Machine Learning in Python Coursera Module 1 Quiz Answer


Offered By

University of Michigan

About this Course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. 


This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

WHAT YOU WILL LEARN

Describe how machine learning is different than descriptive statistics

  • Create and evaluate data clusters
  • Explain different approaches for creating predictive models
  • Build features that meet analysis needs


SKILLS YOU WILL GAIN

  • Python Programming
  • Machine Learning (ML) Algorithms
  • Machine Learning
  • Scikit-Learn



Applied Machine Learning in Python 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?

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 1

• k=3: Class 0


• k=1: Class 0

• k=3: Class 1


k=1: Class 1

k=3: Class 2


• k=1 : Class 0

• 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: (2500, )

y_train: (2500, 50)

X_test: (7500, )

y_test: (7500, 50)


X_train: (10000, 28)

y_train: (10000, )

X_test: (10000, 1 2)

y_test: (10000, )


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, )