- 01. Data Dimensionality
- 02. Trickier Data Dimensionality
- 03. One-Dimensional, or Two?
- 04. Slightly Less Perfect Data
- 05. Trickiest Data Dimensionality
- 06. PCA for Data Transformation
- 07. Center of a New Coordinate System
- 08. Principal Axis of New Coordinate System
- 09. Second Principal Component of New System
- 10. Practice Finding Centers
- 11. Practice Finding New Axes
- 12. Which Data is Ready for PCA
- 13. When Does an Axis Dominate
- 14. Measurable vs. Latent Features Quiz
- 15. From Four Features to Two
- 16. Compression While Preserving Information
- 17. Composite Features
- 18. Maximal Variance
- 19. Advantages of Maximal Variance
- 20. Maximal Variance and Information Loss
- 21. Info Loss and Principal Components
- 22. Neighborhood Composite Feature
- 23. PCA for Feature Transformation
- 24. Maximum Number of PCs Quiz
- 25. Review/Definition of PCA
- 26. Applying PCA to Real Data
- 27. PCA on the Enron Finance Data
- 28. PCA in sklearn
- 29. When to Use PCA
- 30. PCA for Facial Recognition
- 31. Eigenfaces Code