`CountVectorizer` is a feature extraction tool in Natural Language Processing (NLP) that converts a collection of text documents into a matrix of token counts. It tokenizes the input text and builds a vocabulary of known words, then counts the occurrences of each word in the text and constructs a matrix where each row represents a document, and each column represents a word from the vocabulary. 

This matrix can then be used as input to various machine learning models for text classification, sentiment analysis, and other NLP tasks. `CountVectorizer` can also include additional preprocessing steps such as removing stop words and performing stemming or lemmatization.

In this project, we are going to classify spam email according to their content.