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.