Machine learning, better known as “Machine Learning (ML),” is a type of artificial intelligence (AI) that allows software applications to be more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Becoming Competitive at Another Level
Machine learning is important because it gives companies an overview of trends in customer behaviors and business operating models and supports the development of new products. It is now possible to quickly and automatically develop models capable of quickly and accurately analyze large and complex data sets. This is also why leaders such as Amazon, Google and Meta make it a central element in their operations. In short, machine learning has become a key competitive differentiator for many businesses.
Where Can You Use Machine Learning?
There are a multitude of use cases to which machine learning can be applied to reduce costs, mitigate risk, or improve quality of life. It is used in particular in the recommendation of products and services, in cybersecurity to detect anomalies, in health to better manage the burden of nurses, etc.
Moreover, for each use case, there are thousands, and sometimes even millions, of possible models. Despite this impressive number, building good machine learning models is based primarily on data quality. That’s why a multitude of machine learning projects spend the majority of their time cleaning up data and modifying it to make it actionable by algorithms.