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What You Need Before PCA

PCA is fundamentally a linear algebra technique. These prerequisites will help you truly understand dimensionality reduction.

2-3 weeks to prepare

7 topics to cover

3 essential

PCA is built entirely on these concepts

3 topics

eigenvalues

PCA finds eigenvectors of the covariance matrix - this IS PCA

matrices

Covariance matrices and projections are matrix operations

vectors basics

Principal components are directions (vectors) in feature space

For understanding the statistics behind PCA

2 topics

covariance

PCA maximizes variance, captured in the covariance matrix

expectation

Mean centering and variance calculations

Nice to know for practical applications

2 topics

linear

PCA can be viewed as finding the best linear projection

convexity

PCA optimization is convex (has closed-form solution)

Once you've covered the essential prerequisites, you'll have a solid foundation for understanding pca (principal component analysis).