Chapter 1 provided an overview of some of the emerging trends in the industry around Big Data and Artificial Intelligence. We talked about software getting smarter with the application of Artificial Intelligence. In this chapter, we specifically focus on the most popular AI technique for infusing smarts into software—Machine Learning (ML). We see examples of using ML to capture patterns in data and capture these patterns in artifacts called models. We see the three types of ML techniques and discuss applications of each. Finally, in this chapter we review some code examples of building ML models from simple datasets. The code is highly commented, so you can start your own Colaboratory or Jupyter Notebook environment and run the code.
Finding Patterns in Data
As you saw in Chapter 1, AI is all about making computers develop human‐like intelligence. This intelligence can help computers do knowledge representation, learning, planning, perception, language understanding, and more. One of the key areas of AI is Machine Learning, which is all about finding patterns in the data. The human brain is excellent at finding patterns. However, it is not very good at handling lots and lots of data.
Let's look at an example in Listing 2.1. Can you correctly guess the next number in the series?
You should have no trouble looking at this data and finding the pattern. This ...