Classifying Machine Learning Algorithms: A Concise Overview

Machine Learning is a subset of Artificial Intelligence that allows systems to learn and improve from experience without being explicitly programmed. It enables computers to identify hidden patterns and insights in data, making it a powerful tool in various industries. However, with so many machine learning algorithms available, it can be challenging to determine which algorithm is best suited for a particular task. Machine learning algorithms can be broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

In this tutorial, we will discuss briefly the three primary categories of machine learning algorithms:

• Supervised Learning,
• Unsupervised Learning, and
• Reinforcement Learning.

We will also provide lists of commonly used algorithms in each category.

Supervised Learning

Supervised Learning is a type of machine learning where the model is trained on labeled data, where the input features and the corresponding output values are known. The goal is to learn a mapping between the input and output variables so that the model can predict the output for new, unseen data.

Some common supervised learning algorithms include:

• Regression algorithms: Linear Regression, Logistic Regression, Polynomial Regression, Support Vector Regression (SVR), etc.
• Classification algorithms: Decision Trees, Random Forest, Naive Bayes, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), etc.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model is trained on unlabeled data, where only the input features are known. The goal is to find meaningful patterns and structure in the data, without any prior knowledge of the output variables.

Some common unsupervised learning algorithms include:

• Clustering algorithms: K-Means Clustering, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical DBSCAN (HDBSCAN), etc.
• Association rule…