Dimensionality Reduction in Machine Learning: PCA and t-SNE.

Dimensionality reduction is a fundamental concept in machine learning used to reduce the number of input features (dimensions) in a dataset while preserving as much important information as possible. Principal Component Analysis (PCA) Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms data into a new coordinate system. Instead of using the original features, PCA creates new variables called principal components, which are: Linear combinations of the