Machine learning (ML) is seen as a part of artificial intelligence and relates to the study of computer algorithms that improve automatically through experience.

Machine learning algorithms are developed with the aim of making predictions (or decisions) without being explicitly programmed to do so. In other words, it involves computers (machines) learning from the data provided so that they can carry out certain tasks.

There are usually, three main “ingredients” to an ML task:

  • a model or algorithm,
  • sample data, known as “training data“,
  • and validation data

The different types of machine learning algorithms differ in their approaches, the types of data used as input and output, as well as the type of task or problem that they are intended to solve.

Conventionally, ML algorithms are divided in two main categories:

  • Supervised learning algorithms: where the aim is to build a mathematical model of a set of data containing both the inputs and the desired outputs (targets). Examples of supervised algorithms are: classification, regression, similarity learning and active learning.
  • Un-supervised learning algorithms: where the set of data contains only inputs, and the aim is to find structure in the data, e.g. grouping or clustering of data points. Examples of un-supervised algorithms encompass: clustering, anomaly detection, neural networks and latent variable models.

Machine learning algorithms are used in a wide variety of fields and applications:

  • economics and finance,
  • healthcare and drug development,
  • engineering,
  • supply chain and inventory management,
  • marketing,
  • and sport data

ML algorithms are especially useful in situations where it is particularly difficult (or even unfeasible) to develop conventional algorithms to carry out the required tasks.

You can consult some of the projects we have been involved in the past here.

Interested in hearing more? Get in touch today for a free quote!