In this chapter, we have discovered unsupervised learning and its techniques, such as dimensionality reduction and clustering. The main focus was on PCA for dimensionality reduction and several clustering methods, such as k-means clustering, hierarchical clustering, DBSCAN, and spectral clustering. The chapter started with dimensionality reduction and PCA. After PCA, our main focus was on clustering techniques and how to identify the number of clusters. In later sections, we moved on to cluster performance evaluation measures such as the DBI and the silhouette coefficient, which are internal measures. After looking at internal clustering measures, we looked at external measures such as the Rand score, the Jaccard score, the F-measure, and the Fowlkes-Mallows index.
The next chapter, Chapter 12, Analyzing Textual Data, will focus on text analytics, covering the text preprocessing and text classification using NLTK, SpaCy, and scikit-learn. The chapter starts by exploring basic...
