Feature, vector and embedding space

In machine learning, data is everything. However, data in its raw form cannot be directly fed into a machine learning model. It needs to be converted into a numerical representation that the model can understand. This is where feature extraction comes in. Features are numerical representations of raw data that can be used for machine learning. In this article, we will discuss the concepts of feature, vector, and embedding space and their importance in machine learning.

Features

A feature is a me...