In this chapter, we have learned some basic concepts of ML, which is used to solve a real-life problem. We started with a brief introduction to ML including a basic learning workflow, the ML rule of thumb, and different learning tasks, and then we gradually covered important ML tasks such as supervised learning, unsupervised learning, and reinforcement learning. Additionally, we discussed Scala-based ML libraries. Finally, we have seen how to get started with machine learning with Scala and Spark ML by solving a simple classification problem.
Now that we know basic ML and Scala-based ML libraries, we can start learning in a more structured way. In the next chapter, we will learn about regression analysis techniques. Then we will develop a predictive analytics application for predicting slowness in traffic using linear regression and generalized linear regression algorithms.