Support Vector Machines (SVMs) are a type of Supervised Learning algorithm that can be used for both classification and regression tasks. They are particularly useful for problems with high-dimensional data and complex decision boundaries. History SVMs were first introduced in the early 1990s by Vladimir Vapnik and his colleagues at Bell Labs. They were initially…
Logistic regression is a popular algorithm used in supervised learning for classification tasks. Supervised learning is a method of machine learning that involves training a model on a labeled dataset, where the inputs and corresponding outputs are already known. In this article, we will discuss the basics of logistic regression, its applications, and how it…
Linear regression is a supervised learning algorithm that is used to analyze the relationship between a dependent variable and one or more independent variables. It is a widely used method in machine learning and statistics for both simple and multiple linear regression. The goal of linear regression is to find the best-fitting line that represents…
