Machine Learning Fundamentals
Building a Linear Regression Model from Scratch with Python
It’s easy to become consumed by the powerful libraries and pre-built tools available at our fingertips when working on data science and machine learning problems. We often rely on these tools to perform complex tasks and quickly generate insights from data. However, have you ever wondered what happens under the hood of those libraries? How do algorithms like linear regression really work?
Linear regression serves as one of the foundational concepts in machine learning. It’s the proverbial “Hello, World!” of the field. Throughout this article, we’ll break down each component of linear regression, from understanding the mathematical underpinnings of the model to coding its implementation with Python, and we won’t be using any fancy libraries. By implementing it, we gain a deeper grasp of linear regression and the fundamental principles that drive many other machine learning algorithms.
Whether you’re a seasoned data scientist looking to revisit the basics or a budding data scientist, this article promises to be insightful. So, let’s dive in.
Prerequisites
I assume that you have a basic familiarity with Python at the beginner level. While some knowledge of algebra and calculus can be helpful for a deeper understanding, it’s by no means required.
