Practical Data Science using Python - Unsupervised Learning - K-Means Clustering

Practical Data Science using Python - Unsupervised Learning - K-Means Clustering

Assessment

Interactive Video

Information Technology (IT), Architecture

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial introduces K-Means clustering, a popular unsupervised learning algorithm. It explains the difference between supervised and unsupervised learning, providing examples like customer segmentation and document classification. The tutorial also covers the concept of data dimensions and how K-Means clustering groups similar data points to discover patterns. The process of clustering in multi-dimensional data is discussed, emphasizing the importance of determining the number of clusters (K) before applying the algorithm.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary focus of K-Means clustering?

To classify data into predefined categories

To label data with known outcomes

To group similar data points together

To predict future data points

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which type of learning uses labeled data?

Unsupervised learning

Supervised learning

Reinforcement learning

Semi-supervised learning

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In the context of unsupervised learning, what is a common application?

Spam email detection

Customer segmentation

Weather forecasting

Predicting stock prices

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a key characteristic of unsupervised learning?

It uses reinforcement signals

It finds patterns in unlabeled data

It predicts future outcomes

It requires labeled data

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How are data points represented in a multi-dimensional space?

As vectors

As points

As planes

As lines

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What happens to data visualization as the number of dimensions increases?

It becomes more complex

It remains the same

It becomes easier

It becomes impossible

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What must be determined before applying the K-Means algorithm?

The type of data

The number of clusters (K)

The size of the dataset

The algorithm's speed

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