Machine learning is considered a branch of artificial intelligence. Its purpose, as the name suggests, is for machines to be capable of learning—that is, to improve both their use and the user experience through their operation.

What is Machine Learning?

The core idea behind machine learning is that systems improve the user experience on their own through algorithms. In this way, even if a system initially lacks a specific skill, the machine itself develops it based on usage. What is crucial for these systems is that they do this entirely autonomously, without human intervention.

While it may sound like science fiction, it is already a reality. Machines use data and samples from their own usage to enhance their performance without needing to be reprogrammed to acquire a new skill or improve functionality. In other words, the initial program is created as a model capable of generalizing behavior and everything surrounding the performance of its tasks, collecting data that allows it to acquire knowledge. This makes data analysis—from collection to pattern recognition—essential. The key to automated learning is the construction and adaptation of decision trees based on the data previously gathered by the system.

What is Machine Learning Used For?

Machine learning has an incredibly broad range of applications. It can power search engine results on platforms like Bing or Google tailored to user interests, or support medical diagnoses by identifying patterns common to other patients.

Between these extremes—everyday tasks to matters of critical importance—there is a wide spectrum of applications. It can prevent computer security issues, assist with stock market investments, classify genome sequences, recognize people or conversations, create more sophisticated robots, enhance video game experiences, or, of course, improve industrial processes such as collaborative assembly lines, customization, and real-time error detection.