PCA
Back to Home
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
32. PCA Mini-Project Intro
33. PCA Mini-Project
34. Explained Variance of Each PC
35. How Many PCs to Use?
36. F1 Score vs. No. of PCs Used
37. Dimensionality Reduction and Overfitting
38. Selecting Principal Components
Back to Home
31. Eigenfaces Code
Eigenfaces Code
Next Concept