In this chapter, we learned about NLP and graph databases and we learned about the financial concepts that are required to analyze customer data. We also learned about an artificial intelligence technique called ensemble learning. We looked at an example where we predicted customer responses using natural language processing. Lastly, we built a chatbot to serve requests from customers 24/7. These concepts are very powerful. NLP is capable of enabling programs to interpret languages that humans speak naturally. The graph database, on the other hand, is helpful in designing highly efficient algorithms.
In the next chapter, we will learn about practical considerations to bear in mind when you want to build a model to solve your day-to-day challenges. In addition, we also want to look at the practical IT considerations when equipping data scientists with languages to interact with IT developers who put the algorithm to use in real life.
