Different learning styles in Machine Learning Algorithms

Generally, an algorithm can have one or a few types of learning styles, and we’ll discuss them here with examples of algorithms and problem types that suit them.

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

Supervised Learning

As input, data is referred to as training data. It has a label or result attached to it such as Fraud or not Fraud. The process of supervised learning allows providing data and producing outcomes based on prior experiences. Example problems are classification and regression. The following algorithms are examples: Logistic Regression and Decision Trees.

Unsupervised Learning

The data collected here has no labels and we do not know what the outputs will be, so Input data is unlabeled and unknown. Some examples are clustering problems, dimensionality reduction problems, and association rule learning problems. K-Means and Apriori are two examples of algorithms.

Reinforcement Learning

During reinforcement learning, a machine is exposed to an environment in which it needs to constantly train itself by trial and error. This machine’s learning is based on past experience and aims to capture the best possible information to make accurate business decisions. Markov Decision Process as an example of Reinforcement Learning.