What are Machine Learning Algorithms for AI?

Machine learning (ML) algorithms are computer programs that adapt and evolve based on the data they process to produce predetermined outcomes. They are essentially mathematical models that “learn” by being fed data—often referred to as “training data.” Common types of ML algorithms include linear regression and decision trees. Practical applications of ML algorithms include fraud detection and the automatic delivery of personalized marketing offers in retail.

Broadly speaking, there are two main categories of ML algorithms: supervised and unsupervised ML. Supervised ML algorithms involve “teaching” the machine to produce outputs based on its training data, which is already labelled or structured. Unsupervised ML algorithms, on the other hand, work with unstructured data—data that hasn’t already been classified or labeled.

Why Do Machine Learning Algorithms Matter?

ML is the most widely used and fastest-growing subset of AI today. Used to improve a wide array of computing concepts, including computer programming itself, it is often referred to as Software 2.0.

ML algorithms are integrated into just about every kind of device and hardware, from smartphones to servers to watches and sensors. They are increasingly the backbone behind many technological innovations and benefits, from ridesharing to autonomous vehicles to spam filtering, and many more.

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