Practice - Activities
Practice Questions
Test your understanding with targeted questions
What is the main purpose of dataset preparation in unsupervised learning?
💡 Hint: Think about the steps you would need to take to preprocess data.
What does PCA stand for?
💡 Hint: Consider the process of reducing dimensions.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does GMM stand for in unsupervised learning?
💡 Hint: The acronym refers to a statistical concept covering distribution types.
True or False: K-Means assigns each data point to only one cluster.
💡 Hint: Think about how K-Means operates regarding data point classification.
3 more questions available
Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.
- Understanding Gaussian Mixture Models
- Anomaly Detection with Isolation Forest
- An Introduction to PCA
- Isolation Forest for Anomaly Detection
- Introduction to Unsupervised Learning
- Data Preprocessing for Machine Learning
- Understanding Dimensionality Reduction
- Comprehensive Guide to Unsupervised Learning