In this chapter, we learned how to create a data processor pipeline that can be used to train a machine-learning system. We learned how to extract K nearest neighbors to any given data point from a given dataset. We then used this concept to build the K Nearest Neighbors classifier. We discussed how to compute similarity scores such as the Euclidean and Pearson scores. We learned how to use collaborative filtering to find similar users from a given dataset and used it to build a movie recommendation system.

In the next chapter, we will learn about logic programming and see how to build an inference engine that can solve a real world problem.