Summary

This chapter was action packed on machine learning and its various concepts. We covered a lot of theoretical ground in this chapter by learning what machine learning is, some important real-life use cases, types of machine learning, and the important concepts of machine learning such as how we extract and select features, training our models, selecting our models, and tuning them for performance by using techniques such as training/test set and cross validation. We also learnt how we can run our machine learning models specifically on big data and what Spark has to offer on the machine learning side in terms of an API.

In the next chapter, we will dive into actual machine learning algorithms and we will learn a simple yet powerful and popular linear regression algorithm. We will understand it by using an example case study. After studying linear regression we will study another algorithm logistic regression and we will also try to learn it by using a sample case study.