Machine learning, better known as “Machine Learning (ML)” in English, is a type of artificial intelligence (AI) that allows software applications to be more precise at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Become competitive on another level.
Machine learning is important because it gives businesses an overview of trends in customer behavior and business models, and supports the development of new products. It is now possible to quickly and automatically develop models capable of quickly and accurately analyzing 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 in order to reduce costs, mitigate risk, and / or improve quality of life. It is used in particular in the recommendation of products / services, in cybersecurity to detect anomalies, in health to better manage the nursing load, 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. This is why the majority of machine learning projects spend 80% of their time cleaning up data and modifying it to make it usable by algorithms.