In this chapter, we've introduced three popular unsupervised learners able to scale to cope with big data. The first, PCA, is able to reduce the number of features by creating ones containing the majority of variance (that is, the principal ones). K-means is a clustering algorithm able to group similar points together and associate them with a centroid. LDA is a powerful method to do topic modeling on textual data, that is, model the topics per document and the words appearing in a topic jointly.
In the next chapter, we will introduce some advanced and very recent methods of machine learning, still not part of the mainstream, naturally great for small datasets, but also suitable to process large scale machine learning.
