Machine learning is an umbrella term for techniques that enable computers to perform tasks without being explicitly programmed for them. This is particularly useful if requirements are not known beforehand or the circumstances constantly change. In the last years, machine learning gained momentum in the context of big data and predictive analytics where machine learning algorithms are used to find prior unknown patterns in large data sets to gain business insights or build mathematical models to predict the future.

Today, AI is often used as an umbrella term for machine learning, which applies statistical methods and specialized algorithms to large volumes of data so machines can improve their performance (that is, learn). This approach is flexible; it requires no predefined rules, but rather uses training data to learn patterns that exist in real-world applications. For instance, a set of known fraud cases may be used to build a machine-learning model that detects potentially malicious transactions.

Let’s start at the beginning. Machine learning (ML) is a subset of artificial intelligence in which an algorithm discovers patterns in data. These patterns help people or applications predict, classify, or prescribe a future outcome. ML relies on a model, which is essentially an equation that defines the relationship between data inputs and outcomes. ML applies various techniques to create this model, including supervised learning, which studies known prior outcomes, and unsupervised learning, which finds patterns without knowing outcomes beforehand.