CHAPTER 10 / 40
Principal Component Analysis
2 lessons · Explore, experiment and understand.
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2 lessons
Why this matters in ML
PCA finds the axes that capture the most variance — used everywhere from image compression to gene expression analysis to feature engineering pipelines.
- PCA
- whitening
- eigenfaces
- Step 48~20m3D · 3Code · 15★Most cinematic⚡Challenge
PCA: Maximum Variance Perspective
Principal Component Analysis (PCA) is the most-used dimensionality reduction technique and a perfect payoff for everything learned in linear…
Open lesson - Step 49~20m3D · 21Code · 15★Most cinematic⚡Challenge
PCA in Practice
Running PCA on real data requires a handful of practical steps that textbook treatments sometimes skip. First, center the data by subtractin…
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