Count vectorization and Term Frequency-Inverse Document Frequency (TTF-IDF) are two different strategies to convert a bag of words into a feature vector suitable for input to a machine learning algorithm.
Count vectorization takes our set of words and creates a vector where each element represents one word in the corpus vocabulary. Naturally, the number of unique words in a set of documents might be quite large, and many documents may not contain any instances of a word present in the corpus. When this is the case, it's often very wise to use sparse matrices to represent these types of word vectors. When a word is present one or more times, the count vectorizer will simply count the number of times that word ...
