Pca in machine learning: Want to know about Principal Component Analysis

Want to know about Principal Component Analysis ( PCA) in Machine Learning ? Check out this guide for a complete understanding of PCA in Machine Learning . Read on! Learn how to use PCA , a popular unsupervised technique, to transform high-dimensional data into a lower-dimensional representation. See the steps, advantages, disadvantages, and an example of PCA in Python. Principal Component Analysis ( PCA ) is a dimensionality reduction technique used in machine learning and data analysis. It transforms large datasets with many features into smaller sets while keeping the most important information. Learn what is principal component analysis in machine learning , its applications, and how PCA helps in dimensionality reduction. Step-by-step explanation with use cases.