PCA
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 the principal components—orthogonal directions that capture the maximum variance in the data. This helps simplify datasets while retaining 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 complexity and mitigates the curse of dimensionality. It is particularly useful for tasks such as feature extraction, noise reduction, and exploratory data analysis, enabling more efficient model training and improved interpretability of data patterns.