In this chapter, we learned about ML and some of the most popular ML algorithms. The primary goal of ML is to build an analytical model using historical data without much human intervention. ML algorithms can be divided into two categories, namely, supervised learning and unsupervised learning. The supervised learning algorithm relies on labeled data to build models, whereas unsupervised learning uses data that is not labeled. We looked at the k-means cluster analysis algorithm, which is an unsupervised ML algorithm. Of the supervised ML algorithms, we explored decision trees, random forests, and ridge/lasso regression. We also got an overview of using NLP for performing text data analysis.
In the next chapter, we will examine the processing of data in real time and perform data analysis as the data becomes available.