In this chapter, we implemented two end-to-end projects to develop item-based collaborative filtering for movie similarity measurement and model-based recommendation with Spark. We also saw how to interoperate between ALS and MF and develop scalable movie recommendations engines. Finally, we saw how to deploy this model in production.

As human beings, we learn from past experiences. We haven't gotten so charming by accident. Years of positive compliments as well as criticism have all helped shape us into what we are today. You learn what makes people happy by interacting with friends, family, and even strangers, and you figure out how to ride a bike by trying out different muscle movements until it just clicks. When you perform actions, you're sometimes rewarded immediately. This is all about Reinforcement Learning (RL).

The next chapter is all about designing a machine learning project driven by criticisms and rewards. We will see how to apply RL algorithms for developing options trading applications using real-life IBM stock and option price datasets.