One-hot encoding refers to a way of transforming data into vectors where all components are 0, except for one component with a value of 1, e,g.: $$0=[1,0,0,0,0{]}^T$$ $$1=[0,1,0,0,0{]}^T$$ $$\dots$$ $$4=[0,0,0,0,1{]}^T$$ and so on.

One-hot encoding can make it easier for machine learning algorithms to manipulate and learn categorical variables.