The development of technology has allowed advances that in the past were unimaginable. One of them is machine learning. It is a scientific discipline, include inside the principles of Artificial Intelligence, capable of generating systems that learn automatically. This is why when we talk about machine learning we mean device automation learning.

MACHINE LEARNING: HOW IT WORKS?

But how do machines learn automatically?

The machine is able to identify very complex patterns through the data it receives and it’s capable of learning and responding the way as people do; that is in autonomously way, constantly feeding on data and information that the users provide to the system.

To be specific, machine learning actually involves the behavior of an algorithm that reviews the data and has the ability to predict future behaviors. This means that what it learns it’s not the machine itself but the programming the machine has when it feeds from the data and information the user gives to it. The marvelous news is that it does it automatically, meaning that improves autonomously over time.

The process of creating machine learning is to develop devices that are capable of learning for themselves in a time when it is feed with the data that a user is entering and correcting manually. Then, the chosen device starts to extract knowledge from this experience that is constantly monitoring. In short, an algorithm capable of learning autonomously thanks to the analysis of thousands of data constantly reviewed.

The process described before perfectly suits the ability that has certain machine, or, rather the algorithm, to identify complex patterns within a large amount of data.

TYPES OF MACHINE LEARNING

We can find three types of machine learning systems. The first, known as supervised learning, is about devices and systems trained from data and labels. Once the machine has received enough information, it is able to identify new data (produce labels) without adding the label manually.

Machine LearningThere is also a machine learning format that we call unsupervised. It is the model known as clustering, and it is based on the understanding and abstraction of complex patterns from information that is entered directly.

Finally, we can talk about a reinforcement learning, in the cases where the machine learns through experience. It is the case of autonomous cars. The vehicle is penalized each time it makes a wrong decision. With the system of rewards and punishments the vehicle learns to develop the required task.

MACHINE LEARNING FOR COMPANIES

Machine learning allows companies to be much more productive and efficient. We cannot only find it in large projects such as autonomous transportation as we have pointed out before, but there are hundreds of companies that are also already using this to make predictions about the behavior of their customers.

Most companies have a huge amount of data. Machine learning allows us to analyze this data and anticipate possible future actions of your clients. For example, it is able to analyze behaviors and detect potential customers who are about to leave (in the case of a telephone or Internet company). This information allows the company to be proactive and take action before the situation actually happens. How the engine does this? The gadget can, for example, identify users who reduce their consumption until 25% in a month, as likely to leave, although these algorithms are much more specific and accurate to do their specific tasks.

Machine LearningFor a regular person in regular basis, it is impossible to analyze the enormous amount of data generated by companies; instead of investing huge amounts of time and money by doing so, the algorithms are able to detect complex patterns between incredible amounts of data thanks to the variables we provide to them. Machine learning aims to be another tool that allows the harmonious development of work between humans and machines. And it’s here to stay.