Machine learning at a glance

You need three fundamental elements to build a machine learning model: an algorithm, data, and computing power (Figure 1.1). A machine learning algorithm needs to be fed with the right data and trained using the necessary computing power. It can then be used to predict what it has been trained on for unseen data:

Figure 1.1 – The three elements in the machine learning triangle

Machine learning applications can be generally categorized as automation and discovery. In the automation category, the goal of the machine learning model and the software and hardware systems built around it is to do the tasks that are possible and usually easy but tedious, repetitive, boring, or dangerous for human beings. Some examples of this include recognizing damaged products in manufacturing lines or recognizing employees’ faces at entrances in high-security facilities. Sometimes, it is not possible to use human beings for some of these tasks, although the task would be easy. For example, for face recognition on your phone, if your phone was stolen, you would not be there to recognize that the person who is trying to log into your phone is not you and your phone should be able to do it by itself. But we cannot come up with a generalizable mathematical formulation for these tasks to tell the machine what to do in each situation. So, the machine learning model learns how to come up with its prediction, for example, in terms of recognizing faces, according to the identified patterns in the data.

On the other hand, in the discovery category of machine learning modeling, we want the models to provide information and insight about unknowns that are either not easy or fully discovered, or even impossible, for human experts or non-experts to extract. For example, discovering new drugs for cancer patients is not a task where you can learn all aspects of it by going through a couple of courses and books. In such cases, machine learning can help us come up with new insights to help discover new drugs.

For both discovery and automation, different types of machine learning modeling can help us achieve our goals. We will explore this in the next section.