What is machine learning?

If you check out on Wikipedia, you will find a fairly abstract definition of machine learning:

"Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions."

I like to think of machine learning as computer programs that produce different results as they are exposed to more information without changing their source code (and consequently needed to be redeployed). For example, consider a game that I play with the computer.

I show the computer this picture and tell it "Blue Circle". I then show it this picture and tell it "Red Circle". Next I show it this picture and say "Green Triangle."

Finally, I show it this picture and ask it "What is this?". Ideally the computer would respond, "Green Circle."

This is one example of machine learning. Although I did not change my code or recompile and redeploy, the computer program can respond accurately to data it has never seen before. Also, the computer code does not have to explicitly write each possible data permutation. Instead, we create models that the computer applies to new data. Sometimes the computer is right, sometimes it is wrong. We then feed the new data to the computer to retrain the model so the computer gets more and more accurate over time—or, at least, that is the goal.

Once you decide to implement some machine learning into your code base, another decision has to be made fairly early in the process. How often do you want the computer to learn? For example, if you create a model by hand, how often do you update it? With every new data row? Every month? Every year? Depending on what you are trying to accomplish, you might create a real-time ML model, a near-time model, or a periodic model. We will discuss the implications and implementations of each of these in several chapters in the book as different models lend themselves to different retraining strategies.