Understanding Principal Component Analysis

Understanding Principal Component Analysis

A break down of PCA, how it works, and when to use it. The purpose of this post is to give the reader detailed understanding of Principal Component Analysis with the necessary mathematical proofs.

Machine learning (ML) is a subset of artificial intelligence (AI) and it provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The algorithms employed within ML are used to find patterns in data that generate insight and help make data-driven decisions and predictions. These types of algorithms are utilized every day to make critical decisions in medical diagnosis, stock trading, transportation, legal matters and much more. Therefore, it can be seen why data scientists place ML on such a high pedestal; it provides a medium for high priority decisions, that can guide better business and smarter actions, in real-time without much human intervention.

To learn, ML models use computational methods to understand information directly from data without relying on a predetermined equation. The algorithms are made to determine a pattern in data and develop a target function which best maps an input variable, x, to a target variable, y. It must be noted here that the true form of the target function is usually unknown. If the function was known, then ML would not be needed.

Therefore, the idea is to determine the best estimate of this target function by conducting sound inference about the sample data to then apply and optimize the appropriate ML technique for the situation at hand.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.