Kernel density estimation is a method used to estimate the distribution of continuous data without assuming a specific shape. It can accurately model various data shapes and is useful for reducing dimensionality. Two main approaches discussed are independence assumptions and independent component analysis. For missing values, kernel density regression is an effective solution. The Nadaraya-Watson method is a popular nonparametric regression technique.