What is the Machine learning? How it works and how can you use it?

Machine learning is a branch of computer science, that can be considered as a close relative of the artificial intelligence. We can explain this concept with two simple words: “automatic learning”. The intent of this science is to teach computers, robots, and machinery of various kinds to perform actions and activities in the most natural way possible, trying to get closer to human behavior. How can this be done? Learning from experience: machine learning algorithms use mathematical-computational models, that allow machines to learn information directly from data and not from predetermined mathematical models.

With this process, the performance is improved adaptively, based on experience and following examples, optimizing the performance thanks to the progressive increase in the examples to learn from. Machine learning can be understood as the ability of computers and technological machinery to arrive at certain knowledge without having been previously programmed to do so. In fact, it is a series of different mechanisms that allow an intelligent machine to improve its capabilities and performance over time.

• What is machine learning: how and what do machines learn?

If we analyze the issue from an IT perspective, the machine is provided with data, which are processed through algorithms, that develop their own logic to perform the required activity. The programming code is not written in its entirety, to order the vehicle what to do step by step. We can say that machines also work with the same principle that governs human activity: “learn from your mistakes”.

If we want to better define the concept, machine learning takes into consideration some mechanisms that allow an intelligent machine to improve its performance, capabilities and skills over time. The goal is that it learns to perform certain tasks, improving the functions through experience. A series of different algorithms remain at the basis of machine learning: the processing and learning of operations over time will allow the machine to make an increasingly appropriate decision.

Machine learning can be divided into two different sub-categories: supervised learning and unsupervised learning. Supervised learning occurs when a computer is given complete examples to use as directions for performing the required task. Unsupervised learning, on the other hand, takes place when you let a software work without any support or address.

In fact, these are two different learning modes, which differ in the algorithms and in the purpose for which the machines are made. Originally, this subdivision was identified by Arthur Samuel and the learning models are nowadays employed in different ways depending on the machine on which to operate. Mainly, the goal is to achieve the maximum yield and the best result possible. The models are equally effective, the main objective is to adopt the best performance one based on specific needs.

• Supervised learning

The machines are set up with data and information about the desired results. The goal for the system is to identify a general rule that is able to connect the incoming data with the outgoing data. Basically, the machinery is provided with a series of specific notions, that are models created to build a database of experiences and information.

Machine learning of this type ensures that, when facing with a problem, the machine draws on the experiences already included within the system and decides the best answer based on a series of coded experiences. This kind of machine learning is used medical or voice identification industries.

A famous definition of supervised learning was given by developer Adam Geitgey in his article “Machine Learning is Fun”: “In supervised learning, the solving work is left to the computer. Once you understand the mathematical function that led to solving a specific set of problems, it will be possible to reuse the function to answer any other similar problem ”

• Unsupervised learning

In this case, the information is not coded: the machine has the opportunity to draw on a range of information without having had an example of their use. Consequently, the machine itself catalogs all the information, organize it and learn how to use it, learning what the best result is and how to respond to various situations. The machine will identify a logical structure in the various inputs, without having previously labeled them.

• The third way: The reinforcement learning

This is probably the most complex way of machine learning. The machine is equipped with systems that can improve their learning and understand the characteristics of the surrounding environment. A very interesting example to explain how it works are the unmanned cars. The system learns from mistakes, improving performance based on previously achieved results, as well as humans do.

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