The significant amount of corporate information available requires a systematic and analytical approach to select the most important information and anticipate major events.
Machine learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major corporate variables.
Machine learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset.
In business settings, data scientists who perform machine learning do not create an AI in most cases. Instead, they may create a ‘bare bones’ predictive model without any additional bells and whistles. In these instances, the machine does not make an automatic decision; an educated, trained expert makes the final call. The model is used as a decision-making aid.
The science of machine learning plays a key role in the fields of statistics, data mining and artificial intelligence, intersecting with areas of engineering and other disciplines. It also has great applications in fields as diverse as business, medicine, astrophysics, and public policy.