There are four learning styles in machine learning algorithms. Let’s have a look at them −
Supervised learning, one of the most used methods in ML, takes both training data (also called data samples) and its associated output (also called labels or responses) during the training process. The major goal of supervised learning methods is to learn the association between input training data and their labels. For this it performs multiple training data instances.
Based on machine learning based tasks, we can divide supervised learning algorithms in two classes namely Classification and Regression.
Unsupervised learning methods, (opposite to supervised learning methods) do not require any labels or responses along with the training data. Unsupervised ML algorithms learn patterns and relationships from the given raw data. Although these methods may have lots of uncertainties in the result, we can always obtain some useful information such as all kinds of unknown patterns in the data, and the useful features for categorization.
Based on machine learning based tasks, we can divide unsupervised learning algorithms in various classes namely Clustering, Association, Dimensionality Reduction, and Anomaly Detection.
Semi-supervised machine learning methods, as name entails, are neither fully supervised or unsupervised learning methods. For training purposes, semi-supervised methods use a small amount of pre-labeled annotated data and lots of unlabeled data. To implement such learning methods, we can follow one of the two approaches given below −
In reinforcement learning methods, a trained agent interacts with a specific environment and takes actions based upon the current state of that environment. Based on the action taken, the agent will get reward or penalty. This process is repeated until the agent learns and adopts optimal policy.