Practice Activities - 3.2 | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10) | Machine Learning
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3.2 - Activities

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main purpose of dataset preparation in unsupervised learning?

💡 Hint: Think about the steps you would need to take to preprocess data.

Question 2

Easy

What does PCA stand for?

💡 Hint: Consider the process of reducing dimensions.

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 in unsupervised learning?

  • Gaussian Model Model
  • Gaussian Mixture Model
  • General Multivariable Model

💡 Hint: The acronym refers to a statistical concept covering distribution types.

Question 2

True or False: K-Means assigns each data point to only one cluster.

  • True
  • False

💡 Hint: Think about how K-Means operates regarding data point classification.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are analyzing a dataset of customer transactions and suspect that some transactions could be fraudulent. Describe how you would apply Isolation Forest effectively in this scenario.

💡 Hint: Focus on how preprocessing affects the model's performance.

Question 2

Imagine you have a high-dimensional dataset. Explain how and why you would choose between PCA and Feature Selection for dimensionality reduction.

💡 Hint: Consider interpretability vs dimensionality reduction metrics.

Challenge and get performance evaluation