Practice Module 5: Unsupervised Learning & Dimensionality Reduction (5) - Unsupervised Learning & Dimensionality Reduction (Weeks 9)
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Module 5: Unsupervised Learning & Dimensionality Reduction

Practice - Module 5: Unsupervised Learning & Dimensionality Reduction

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary focus of unsupervised learning?

💡 Hint: Think about the difference between supervised and unsupervised learning.

Question 2 Easy

What does a centroid represent in clustering?

💡 Hint: It's like the middle point of a group.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What type of data does unsupervised learning utilize?

Labeled data
Unlabeled data
Structured data

💡 Hint: Consider what distinguishes it from supervised learning.

Question 2

True or False: K-Means requires you to specify the number of clusters in advance.

True
False

💡 Hint: Reflect on how K-Means starts its process.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with various characteristics, explain how you would determine the optimal number of clusters using both the Elbow method and Silhouette analysis.

💡 Hint: Both methods approach the problem from different angles.

Challenge 2 Hard

Suppose you have customer data with missing values and categorical features. Describe how you would preprocess this data for K-Means and DBSCAN.

💡 Hint: Consider the nature of each algorithm while preprocessing.

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Reference links

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