A Cheat Sheet to Machine Learning Algorithms: Types, Applications, and Examples
Machine learning is a field of artificial intelligence that involves training algorithms to learn from and make decisions based on data. There are three main categories of machine learning algorithms: unsupervised, supervised, and semi-supervised. In this article, we will provide a detailed overview of the main machine learning algorithms in each of these categories. We will explain how each algorithm works, and provide examples of how they can be applied. By the end, you will have a good understanding of the different types of machine learning algorithms and how to choose the right algorithm for your specific problem.
Unsupervised Learning Algorithms
Unsupervised learning algorithms are used when we have a dataset that is not labeled or classified. These algorithms are used to find patterns and structure in the data and group similar observations together.
Clustering Algorithms
- K-Means: K-means is a popular clustering algorithm that groups data into k clusters based on similarity. For example, let’s say we have a dataset of student test scores, and we want to group the students into different clusters based on their scores on different subjects. K-means would start by selecting k initial cluster…
