Machine learning can be split into two different models, both of which entail training a machine on a database that’s integrated, structured and then analyzed (data crunching).
In supervised learning, the machine relies on human intervention.
The person provides the bases of the machine’s knowledge so it can then understand how to use them and propose improvements, which will be systematically validated by a human before being implemented.
In unsupervised learning, the machine doesn’t require this human validation component. It performs the research, identifies new knowledge and memorizes it all on its own, as long as the mathematical thresholds supplied to it are respected.
