What is Machine Learning?

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

We could also say that ML is a scientific discipline in the field of Artificial Intelligence that creates systems that learn automatically. Learning in this context means identifying complex patterns in millions of pieces of data. The machine that really learns is an algorithm that reviews the data and is able to predict future behavior. Automatically, also in this context, implies that these systems improve autonomously over time, without human intervention. Let’s see how it works.

Big Data and Machine Learning applied to the company

A telephone company wants to know which customers are in “danger” of noticing their service cancellation in order to carry out commercial actions that prevent them from going to the competition. How can you do it? The company has a lot of customer data, a lot: seniority, contracted plans, daily consumption, monthly calls to customer service, latest changes in contracted plans… but it surely uses them only for billing and statistics. What else can be done with that data? They can be used to predict when a customer is going to unsubscribe and manage the best action to avoid it. In a nutshell, with Machine Learning you can go from being reactive to being proactive. The historical data of all customers, duly organized and processed in blocks, generate a database that can be exploited to predict future behavior, favoring those that improve business objectives and avoiding those that are harmful.

That amount of data is impossible for a person to analyze to draw conclusions and even less to make predictions. Algorithms, on the other hand, can detect patterns of behavior by counting on the variables that they provide and discover anomalies that have led, in this case, to realizing they have unsubscribed as a customer.