In this chapter, we saw our first usage of Gensim's machine learning algorithms, and in particular, topic models. Topic models are a great way for us to work with unlabeled data, and they help us find underlying structures in text. There are multiple ways for us to identify topics in the text, with LDA, LSI, HDP, and NNMF being the most popular methods, and we have discussed ways to use all these methods in both scikit-learn and Gensim.
In the next chapter, we will move into advanced operations using topic models.
