Practice Week 10: Advanced Unsupervised & Dimensionality Reduction (2) - Unsupervised Learning & Dimensionality Reduction (Weeks 10)
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Week 10: Advanced Unsupervised & Dimensionality Reduction

Practice - Week 10: Advanced Unsupervised & Dimensionality Reduction

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does GMM stand for?

💡 Hint: Think about clustering.

Question 2 Easy

Name one application of anomaly detection.

💡 Hint: Consider financial transactions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a key advantage of Gaussian Mixture Models over K-Means?

A. It's faster.
B. It allows soft assignments.
C. It requires labeled data.

💡 Hint: Think about the flexibility of GMMs.

Question 2

True or False: t-SNE can capture global structures in high-dimensional data.

True
False

💡 Hint: Recall the purpose of t-SNE.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

As an analyst, you need to choose between GMM and K-Means for a dataset believed to have overlapping clusters. Justify your choice based on the characteristics of your data.

💡 Hint: Consider shapes and cluster distributions.

Challenge 2 Hard

You are tasked with detecting fraud in a dataset with imbalanced classes (few fraud cases). Which anomaly detection algorithm would you favor, Isolation Forest or One-Class SVM, and why?

💡 Hint: Reflect on how each algorithm handles data imbalance.

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Reference links

Supplementary resources to enhance your learning experience.