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Tutorial 2: Principal component analysis

Difficulty level
Beginner
Duration
6:33

This tutorial covers how to perform principal component analysis (PCA) by projecting the data onto the eigenvectors of its covariance matrix.

Overview of this tutorial:

  • Calculate the eigenvectors of the sample covariance matrix.
  • Perform PCA by projecting data onto the eigenvectors of the covariance matrix.
  • Plot and explore the eigenvalues.

To quickly refresh your knowledge of eigenvalues and eigenvectors, you can watch this short video (4 minutes) for a geometrical explanation. For a deeper understanding, this in-depth video (17 minutes) provides an excellent basis and is beautifully illustrated.

Topics covered in this lesson
  • How to calculate the eigenvectors of the sample covariance matrix
  • How to perform PCA by projecting the data onto the eigenvectors
  • PCA implementation