Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
Channel: CodeEmporium
Date: 2018-10-23

In this video, we are going to see exactly how we can perform dimensionality reduction with a famous Feature Extraction technique - Principal Component Analysis PCA. We’ll get into the math that powers it

REFERENCES 
[1] Computing Eigen vectors and Eigen values: https://www.scss.tcd.ie/~dahyotr/CS1BA1/SolutionEigen.pdf
[2] Diagonalizing a Matrix: http://mathworld.wolfram.com/MatrixDiagonalization.html
[3] Step by step diagonalization: https://yutsumura.com/how-to-diagonalize-a-matrix-step-by-step-explanation/#Step_2_Find_the_eigenvalues

  
IMAGE REFERENCES
[1] Gene Expression: https://geneed.nlm.nih.gov/topic_subtopic.php?tid=15&sid=22
[2] Graph_plot: https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/140579#140579
[3] Eigenvecotrs: https://commons.wikimedia.org/wiki/File:Eigenvectors.gif