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Machine learning (ML) refers to a subset of Artificial Intelligence (AI) and in particular refers to a method of data analysis that allows computers and other IT systems to acquire new information without having been explicitly and preventively programmed.

Initially, the term was coined in 1959 by Arthur Samuel, an American pioneer scientist in the field of Artificial Intelligence. Today, however, the most accredited definition is the one by Tom Michael Mitchell, director of the ML department of Carnegie Mellon University. In short words:

Machine learning allows computers to learn from experience, this means that there is learning (experience) when the program performance improves after performing a task or completing an action.

Now, the areas of application of Machine Learning are the most varied, from Manufacturing to Retail, from financial services to E-Commerce; in the healthcare, construction, energy, and beyond sectors.

Thanks to ML, organizations can make better decisions without the need for human intervention. Machine learning technology is used to automate repetitive activities, accelerate time to value, obtain better business visibility, and greater collaboration.

Among the various technologies that are part of the world of Machine Learning, Deep Learning is increasingly popular. It is a methodology based on artificial neural networks organized on different levels where the information learned at each level, is transferred to the next and so on, up to – in depth – more complete information.

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