Classification is a method used in Supervised Learning, which aims at outputting a discrete variable, such as a class or a label. In this article, we will be diving into the main classification algorithms and the mathematics behind them.  

Classification is a method used in Supervised Learning, which aims at outputting a discrete variable, such as a class or a label. This article is the second part to the previous article and looks at the remaining classification algorithms.

In the more recent years, Linedata implemented machine learning algorithms in front-office products as well as in back-office solutions. Thus, supervised classification learning is at the heart of some of Linedata’s main products and we will now explore one of the use cases.

Unsupervised learning is a branch of machine learning, characterized by the analysis and clustering of unlabeled datasets. To do so, these algorithms learn to find patterns or groups in the data, with little to no human intervention. In this article, we will understand what unsupervised machine learning is and look at some of its main applications.

Unsupervised learning is one of the branches of machine learning. It identifies clusters or groups based on an unlabeled dataset, with as little human intervention as possible. In this article, we will dive into the main unsupervised learning algorithms, which are used for some everyday applications.