-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating

Mastering NLP from Foundations to LLMs
By :

Here, we will explain some of the most common machine learning models, as well as their advantages and disadvantages. Knowing this information will help you pick the best model for the problem and be able to improve the implemented model.
Linear regression is a type of supervised learning algorithm that’s used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the input features and the output. The goal of linear regression is to find the best-fit line that predicts the value of the dependent variable based on the independent variables.
The equation for a simple linear regression with one independent variable (also called a simple linear equation) is as follows:
Here, we have the following: