Practice Implement K-means Clustering With Optimal K Selection (5.7.3) - Unsupervised Learning & Dimensionality Reduction (Weeks 9)
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Implement K-Means Clustering with Optimal K Selection

Practice - Implement K-Means Clustering with Optimal K Selection

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does K in K-Means stand for?

💡 Hint: Think about the meaning of 'K'.

Question 2 Easy

What is the purpose of the centroid in K-Means?

💡 Hint: Consider what a centroid does in geometry.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is K-Means used for?

Supervised learning
Unsupervised learning
Reinforcement learning

💡 Hint: Think about what 'unsupervised' means.

Question 2

True or False: The Elbow method requires you to know the number of clusters in advance.

True
False

💡 Hint: Consider the function of the Elbow method.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with varying densities of clusters, discuss the challenges and strategies for applying K-Means.

💡 Hint: Think about how data normalization might help.

Challenge 2 Hard

Design a scenario where the Elbow method may yield ambiguous results and how you would handle it.

💡 Hint: Consider situations where K values may show gradual improvement.

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Reference links

Supplementary resources to enhance your learning experience.