Machine Learning - Overview - Type Of Machine Learning Tutorial

There are three types of machine learning:

Supervised Learning

In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.

Supervised Learning is divided into two parts i.e Regression and Classification

If the target variable is numeric then it of Regression type.

If the target variable is categorical then it is of classification type.

Unsupervised Learning

In unsupervised learning, we don't have labeled data. A model can identify patterns, anomalies, and relationships in the input data.

Unsupervised Learning is divided into two parts i.e Clustering and Association

Clustering -  Clustering is a method of grouping objects into clusters such that objects with the most similarities remain in a group and have less or no similarities with the objects of another group.

Association - Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. 

For example, on reviewing each bill of a shopping mall, you will find whenever milk is purchased, at the same time egg is also purchased. This means there is a strong association between eggs and milk, and you will place both items at the same place for more profit.

Dimensionality Reduction – It is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.

Anomaly Detection - Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the normal data points.

Reinforcement Learning

Using reinforcement learning, the model can learn based on the rewards it received for its previous action.

Consider an environment where an agent is working. The agent is given a target to achieve. Every time the agent takes some action toward the target, it is given positive feedback. And, if the action taken is going away from the goal, the agent is given negative feedback. 

Example – Self-Driving Car, Alpha G