In this chapter we have covered details around machine learning basics, types of machine learning, introduction to Spark MLLib, introduction to Pipeline API, examples of building a Pipeline API and then highlighting the algorithms provided by Spark around feature engineering, classification, regression, clustering and collaborative filtering.
Machine learning is an advanced topic, and it is impossible to cover the depth and breadth of the topic in such a small chapter. However, I hope this chapter gives you a flavor of what is available within Spark and where you can go to for further information. The references section contains the details of the topics. For machine learning, I would recommend Practical Machine Learning or Master Machine Learning with Spark both of which have been published by Packt Publishing and are really good books to give your more in-depth understanding of machine learning.
The next chapter covers GraphX, which is quite a hot topic. We'll cover the basics of...