Today we learnt about the PCA

What is PCA?

Principal Component Analysis (PCA) in Machine Learning? Reducing the number of variables in a data collection while retaining as much information as feasible is the main goal of PCA. PCA can be mainly used for Dimensionality Reduction and also for important feature selection.

Benefits of the PCA

1) Faster Execution

2) Visualization

Step by Step to do PCA

-> Geometric Intuition

-> Mean and Variance

-> Problem formulation

->Covariance and Variance

->Covariance matrix

->Linear transformer of eigen vector and eigen value

And their implementation on google using pyhthon

anyone have problem in PCA then free to ask me in theory and coding both .

feel free to ask