6.2 - Dimensionality Reduction
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What does dimensionality reduction aim to achieve?
💡 Hint: Think about how high dimensions can complicate data analysis.
What is PCA primarily used for?
💡 Hint: Consider PCA's role in simplifying data.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does dimensionality reduction primarily address?
💡 Hint: Focus on what dimensionality reduction aims to solve.
True or False: PCA assumes the relationships in data are non-linear.
💡 Hint: Think about the nature of PCA.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Design an experiment where dimensionality reduction would enhance the analysis of a high-dimensional dataset. Outline the steps you would take and justify your choices.
💡 Hint: Think about the dataset characteristics and how dimensionality reduction can clarify analysis.
Investigate a real-life application that uses t-SNE or UMAP. Describe the data, the dimensionality reduction method, and the impact it had on results.
💡 Hint: Consider exploring academic papers or project reports showcasing these applications.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.