What is PCA? ( Definition, Intuition, Example).The contents of this article are as follows: This article will take us through the concept of PCA transformation intuitively with live examples that will be easy to remember and understand. Principal Component Analysis does this transformation of extracting the most important information out of our dataset and removing the rest (mostly termed as noise). A cat vs dog classification using high-definition images would be almost as good as one using 480p images. One way to help the machine learning algorithms train faster is to give them a smaller dataset (low dimensional data, less features). The applications have become a lot more mainstream with the advent of advancements in machine learning. PCA has long been a prominently used technique in data science for the purpose of dimensionality reduction.
