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L5: Applications of Machine Learning

Total questions: 17

Worksheet time: 11mins

Name
Class
Date
1.

Manual Feature Extraction is present in which of the following

a)

Machine Learning

b)

Deep Learning

2.

Which field emerged to address the problem of learning functions from data without explicit programming?

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

3.

Select the characteristics of Deep Learning.

a)

Automatic Feature Extraction

b)

Excels with raw, unstructured data

c)

Massive amounts of data

d)

Very Less Computational Power

4.

Pick the Deep Learning applications

a)

Language Translation

b)

Image Recognition

c)

Speech recognition

d)

Timeseries Forecasting

5.

Which of these is NOT a parameter for Azure’s Neural Network Regression?

a)

Hidden Layer Specification

b)

Number of hidden Nodes

c)

Number of hidden layers

d)

Learning Rate

6.

What category does Markov decision process fall into?

a)

Reinforcement learning

b)

Supervised learning

c)

Unsupervised learning

d)

Semisupervised learning

7.

Which of these do not have an unsupervised approach?

a)

Feature Learning

b)

Text Classification

c)

Forecasting

d)

Similarity Learning

8.

Which of these do have a supervised approach?

a)

Feature Learning

b)

Text Classification

c)

Forecasting

d)

Similarity Learning

9.

Which of these isn’t used for collaborative filtering?

a)

Explicit identifiers like your ratings

b)

Implicit identifiers like your browsing history

c)

User features like age

d)

Item features like manufacturer

10.

Choose the techniques are part of Text Normalization

a)

Tokenization

b)

Stemming

c)

Lemmatization

d)

Phrase Chunking

11.

Which of the following are the typical steps in model training for text classification?

a)

Text Normalization

b)

Document Labeling

c)

Feature Extraction

d)

Supervised Learning model training

12.

Feature Learning is also know as

a)

Recreation Learning

b)

Representation Learning

c)

Responsive Learning

d)

Regressive Learning

13.

"Feature Learning" is used to transform sets of inputs to new potential useful inputs to solve the given problem.

a)

True

b)

False

c)

Can't say

d)

None

14.

Select the algorithms used in Unsupervised feature learning

a)

Principal component analysis(PCA)

b)

Autoencoder(deep Learning)

c)

Independent component analysis

d)

Matrix Factorization

15.

Which of these is NOT a forecasting algorithm?

a)

ARIMA

b)

Clustering

c)

Prophet

d)

Multivariate Regression

16.

Anomaly detection is an Unsupervised Learning Algorithm only

a)

True

b)

False

17.

Which of these are properties of CNNs?

a)

Translational Invariance

b)

Ability to learn increasingly complex patterns

c)

Restricted to certain types of patterns