# Introduction to Machine Learning: How We Arrive at Linear Regression

The article explains that machine learning is a branch of artificial intelligence where computers learn patterns from data rather than following explicitly programmed rules. It introduces three main types of machine learning—supervised, unsupervised, and reinforcement learning—and focuses on supervised learning for regression problems, which predict continuous numerical values. Linear Regression is presented as a simple supervised learning algorithm that finds a linear relationship between input and output variables to draw a best-fit line for making predictions.

Before we talk about Linear Regression, we first need to understand the bigger idea it belongs to Machine Learning. Machine Learning is the reason applications today can: But what exactly is Machine Learning? Machine Learning is a branch of Artificial Intelligence where we teach computers to learn patterns from data instead of explicitly programming every rule. In traditional programming: You give the computer rules + data → it gives you answers. In Machine Learning: You give the computer data + answers → it learns the rules. Think of teaching a child: You say: You must manually define every rule. You show the child many examples: Eventually, the child learns the pattern: “Oh… adding numbers follows a pattern.” That is exactly how Machine Learning works. The main goal is simple: To help machines learn patterns from data and make predictions on new, unseen data. There are three main types: The model learns from: Example: This is where Linear Regression belongs. The model is given data without answers and tries to find patterns on its own. Example: The model learns through: Example: Once we focus on supervised learning, we usually ask questions like: These are called regression problems. A regression problem is when we try to predict a continuous numerical value. Examples: This is different from classification, where we predict categories like: Now that we understand regression problems, we can introduce one of the simplest solutions: Linear Regression Linear Regression is a supervised learning algorithm used to predict continuous values by finding a relationship between input and output variables. Because many real-world relationships can be approximated using a straight line. Example: These relationships often follow a pattern that can be simplified as: “As X increases, Y also increases or decreases in a predictable way.” Linear Regression tries to draw a best-fit line through data points. This line is used to: Mathematically, it is written as: y = mx + c Next we will do a deep dive into Linear Regression; Buckle up