Principal Component Analysis
Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and data visualization in machine learning and data science. It transforms high-dimensional data into a lower-dimensional form by identifying orthogonal axes (principal components) that capture the maximum variance in the data. This helps simplify datasets while preserving as much information as possible.
Developers should learn PCA when working with high-dimensional data in fields like machine learning, data analysis, or image processing, as it reduces computational costs and mitigates overfitting. It is particularly useful for exploratory data analysis, feature extraction, and noise reduction in applications such as facial recognition, genomics, and financial modeling. Understanding PCA is essential for optimizing model performance and interpreting complex datasets.