Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of input variables or features in a dataset. It involves transforming high-dimensional data into a lower-dimensional space while preserving as much of the original information as possible. This can help improve the efficiency and effectiveness of machine learning algorithms, reduce computational costs, and simplify the interpretation of data. Dimensionality reduction techniques include principal component analysis, t-SNE, and autoencoders.