{"slug": "introduction-to-machine-learning-how-we-arrive-at-linear-regression", "title": "# Introduction to Machine Learning: How We Arrive at Linear Regression", "summary": "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.", "body_md": "Before we talk about Linear Regression, we first need to understand the bigger idea it belongs to Machine Learning.\nMachine Learning is the reason applications today can:\nBut what exactly is Machine Learning?\nMachine Learning is a branch of Artificial Intelligence where we teach computers to learn patterns from data instead of explicitly programming every rule.\nIn traditional programming:\nYou give the computer rules + data → it gives you answers.\nIn Machine Learning:\nYou give the computer data + answers → it learns the rules.\nThink of teaching a child:\nYou say:\nYou must manually define every rule.\nYou show the child many examples:\nEventually, the child learns the pattern:\n“Oh… adding numbers follows a pattern.”\nThat is exactly how Machine Learning works.\nThe main goal is simple:\nTo help machines learn patterns from data and make predictions on new, unseen data.\nThere are three main types:\nThe model learns from:\nExample:\nThis is where Linear Regression belongs.\nThe model is given data without answers and tries to find patterns on its own.\nExample:\nThe model learns through:\nExample:\nOnce we focus on supervised learning, we usually ask questions like:\nThese are called regression problems.\nA regression problem is when we try to predict a continuous numerical value.\nExamples:\nThis is different from classification, where we predict categories like:\nNow that we understand regression problems, we can introduce one of the simplest solutions:\nLinear Regression\nLinear Regression is a supervised learning algorithm used to predict continuous values by finding a relationship between input and output variables.\nBecause many real-world relationships can be approximated using a straight line.\nExample:\nThese relationships often follow a pattern that can be simplified as:\n“As X increases, Y also increases (or decreases) in a predictable way.”\nLinear Regression tries to draw a best-fit line through data points.\nThis line is used to:\nMathematically, it is written as:\ny = mx + c\nNext we will do a deep dive into Linear Regression; Buckle up!", "url": "https://wpnews.pro/news/introduction-to-machine-learning-how-we-arrive-at-linear-regression", "canonical_source": "https://dev.to/moraa_omwoyo/-introduction-to-machine-learning-how-we-arrive-at-linear-regression-hhc", "published_at": "2026-05-23 22:28:09+00:00", "updated_at": "2026-05-23 22:31:44.360751+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/introduction-to-machine-learning-how-we-arrive-at-linear-regression", "markdown": "https://wpnews.pro/news/introduction-to-machine-learning-how-we-arrive-at-linear-regression.md", "text": "https://wpnews.pro/news/introduction-to-machine-learning-how-we-arrive-at-linear-regression.txt", "jsonld": "https://wpnews.pro/news/introduction-to-machine-learning-how-we-arrive-at-linear-regression.jsonld"}}