Kernel Methods

Kernel Methods are a collection of mathematical approaches used primarily for non-linear data modeling. They create non-linear mappings of data which allow for more intricate and subtle pattern recognition. Kernel Methods are used in a variety of sectors, such as healthcare, finance, and transportation, as they enable complex learning tasks such as classification, clustering, and anomaly detection. Kernel Methods enable data to be processed effectively and efficiently, while also providing valuable information to enable actionable insights to be drawn from easily. As a result, they are valuable tools for making data-driven decisions.