# Introduction to Machine Learning: How We Arrive at Linear Regression
The article introduces the concept of Machine Learning and its relation to Linear Regression. It explains how Machine Learning allows computers to learn from data rather than following explicit programming rules. The article also outlines the types of Machine Learning and the significance of regression problems in predicting continuous numerical values.
- ▪Machine Learning is a branch of Artificial Intelligence that teaches computers to learn patterns from data.
- ▪There are three main types of Machine Learning: supervised, unsupervised, and reinforcement learning.
- ▪Linear Regression is a supervised learning algorithm used to predict continuous values by finding relationships between input and output variables.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/moraa_omwoyo/-introduction-to-machine-learning-how-we-arrive-at-linear-regression-hhc |
| Publication time | Sat, 23 May 2026 22:28:09 +0000 |
| Retrieval time | 2026-05-23T22:37:28.593Z |
| Last seen | 2026-05-23T22:37:28.593Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | t9Sd0oU479Bs |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3708630) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Stacy Omwoyo Posted on May 23 # Introduction to Machine Learning: How We Arrive at Linear Regression #beginners #python #datascience #machinelearning 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: recommend movies on Netflix, suggest products on Amazon, recognize faces on your phone, and even predict house prices or exam scores.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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