Linear regression is likely the first algorithm that you would learn when starting down a career path in data science or AI, because it’s simple to implement and easy to apply in real-time.
Here’s a great primer on how to do linear regression in TensorFlow 2.0.
This algorithm is widely used in data science and statistical fields to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Several types of regression techniques are available based on the data being used. Although linear regression involves simple mathematical logic, its applications are put into use across different fields in real-time. In this article, we’ll discuss linear regression in brief, along with its applications, and implement it using TensorFlow 2.0.