Principal Component Analysis (PCA) Tutorial
In data science, one of the most often used linear model to extract information from data is Principal Component Analysis (PCA). In this tutorial, you will learn about what PCA is and how PCA can be useful for you in term of data preprocessing, feature extraction, dimensional reduction, and data visualization. The tutorial provides well-organized knowledge on the mechanics of the Principal Component Analysis, including step-by-step computations with a lot of numerical examples. Basic knowledge on Linear Algebra is necessary to understand the numerical examples. The brief knowledge on linear algebra are provided in the appendix.
The topics of the tutorial are as follow:
What is PCA?
Why do we need PCA?
How does PCA works?
What are the PCA algorithms?
PCA Numerical Examples
How to compute Mean corrected data matrix?
How to calculate Standardized Data?
How to calculate Covariance Matrix from your data?
How to calculate Correlation Matrix from your data?
How to obtain Principal Component Analysis (PCA) from Covariance Matrix?
How to obtain Principal Component Analysis (PCA) from Correlation Matrix?
How to obtain Principal Component Analysis (PCA) based on Singular Value Decomposition (SVD)?
Free PCA online Interactive Programs
PCA in Microsoft Excel (using PCA Excel Add-In)
PCA in Python
How many PCA components should we use?
How to interpret PCA results?
What are the applications of PCA?
What are the strength and Weaknesses of PCA?
Brief Linear Algebra
Resources on PCA