Machine learning is a technology that teaches computers and robots to learn from experience, using a mathematical-computational approach.

The machine learning system is based on a set of learning algorithms that allow the machines to establish a continuous learning process, to make them able to perform a task without being programmed in advance, thus making them similar to the human experiential learning process, which is able to judge the situation based on its past experiences.


When we talk about machine learning, we do not refer to a single form of artificial intelligence. Each application is based on specific machine learning models and on different techniques according to the final objective. There are various types of machine learning:
  • Supervised learning: the algorithm is instructed through a set of structured data containing information about input and output. This type of machine learning is appropriate for classifying objects as well as for recognising patterns already identified before. Since you already know the outcomes, supervised machine learning is more appropriate to be used for problems concerning the real-world.
  • Unsupervised learning: there are unstructured data about the input and no information about the output. It is an automatic learning system that processes raw data, detecting correlations that human beings would not be able to analyse. Unlike supervised learningunsupervised machine learning applications are more appropriate when it comes to unknown patterns which you don’t have any data about.
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Machine learning offers a new perspective in terms of artificial intelligence, guaranteeing the possibility of generic solutions to very wide and articulated problems that are impossible to be solved otherwise, due to the multitude of variables to be analysed. The machine learning applications are numerous. Here you are some:

Marketing: in this context machine learning is used for the analysis of users’ and customers’ habits, anticipating their behaviour and providing solutions to their needs.

Healthcare: in the health sector machine learning has the aim of improving the living conditions of patients. Some concrete applications are those programmes able to analyse the characteristics of the patients and to evaluate the variations of the measured parameters. The aim is to arrive at the predictability of the emergence of critical conditions, thus reducing the hospitalisation of the patients.

Cybercrime: machine learning increases the security of corporate and public networks, making attacks by external parties more difficult. The software studies behaviours looking for malware attacks and preventing intrusion into networks.

Supply Chain Management: machine learning in supply chain management allows monitoring and analysing the entire supply and distribution chain and, at the same time, connecting all the players in the chain.