Practice Lab: Exploring Advanced Unsupervised Learning and Applying PCA for Data Reduction - 3 | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does GMM stand for?

πŸ’‘ Hint: Remember the clustering model we discussed.

Question 2

Easy

Name one application of PCA.

πŸ’‘ Hint: Think about how we represent complex data.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does GMM stand for?

  • Gaussian Mixture Model
  • Generalized Mixture Model
  • Gaussian Model Method

πŸ’‘ Hint: Focus on the probabilistic nature of this model.

Question 2

True or False: PCA aims to reduce the number of variables while retaining as much variance as possible.

  • True
  • False

πŸ’‘ Hint: Think about what PCA seeks to preserve.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a dataset with numerous features. Explain how dimensionality reduction via PCA could impact your machine learning model's performance and computational efficiency.

πŸ’‘ Hint: Focus on the interplay between complexity and interpretability.

Question 2

You have a dataset suspected to have non-standard clusters. Would you employ K-Means or GMM? Justify your answer with concepts from this section.

πŸ’‘ Hint: Evaluate the cluster shapes you anticipate encountering.

Challenge and get performance evaluation