The bag-of-words model represents a corpus literally as a bag of words, not taking into account the position of the words—only their count. Stop words are common words such as "a", "is," and "the", which don't add information value.
TF-IDF scores can be computed for single words (unigrams) or combinations of multiple consecutive words (n-grams). TF-IDF is roughly the ratio of term frequency and inverse document frequency. I say "roughly" because we usually take the logarithm of the ratio or apply a weighting scheme. Term frequency is the frequency of a word or n-gram in a document. The inverse document frequency is the inverse of the number of documents in which the word or n-gram occurs. We can use TF-IDF scores for clustering or as a feature of classification. In the Extracting topics with non-negative matrix factorization recipe, we will use the scores to discover topics.
NLTK represents the scores by a sparse matrix with one row for each...