Principal Component Analysis (PCA) in Machine Learning

In this video, we have covered Principal Component Analysis (PCA) from Machine learning in detail. 

The primary purpose of a principal component analysis is to reduce the number of dimensions in a variety of artificial intelligence applications, such as computer vision and image compression. 

In situations when the data has large dimensions, it can also be used to uncover hidden patterns. Principal Component Analysis is used in a variety of disciplines, including finance, data mining, psychology, and others.

The main step involved in the Principal Component Analysis is given below:

• At first you need to Standardize the dataset.
• Compute the covariance matrix for the features in the dataset.
• Compute the eigenvalues as well as the eigenvectors for the covariance matrix.
• Sort the eigenvalues ​​and their corresponding eigenvectors. 
• Choose k eigenvalues ​​to form an eigenvector matrix.
• Transform the original matrix.

Principal Component Analysis: https://www.geeksforgeeks.org/ml-principal-component-analysispca/