---
title: "What Is Machine Learning? A Plain-English, Sourced Explainer"
slug: what-is-machine-learning
category: ai
category_label: "AI"
author: "BrainWavePost Staff"
date: 2026-06-04
tags: ["machine learning", "AI basics", "explainer"]
read_time_minutes: 6
canonical_url: https://brainwavepost.com/article/what-is-machine-learning
source: BrainWavePost
---

# What Is Machine Learning? A Plain-English, Sourced Explainer

*AI · 2026-06-04 · BrainWavePost Staff · 6 min read*

> A short, fully sourced introduction to machine learning using official definitions from IBM, Google and the U.S. National Institute of Standards and Technology.

> **How this article is sourced** _(info)_
>
> Every definition here comes from IBM's official 'What is machine learning?' page, Google's Machine Learning Crash Course, and NIST's AI Risk Management Framework glossary. [1][2][3]

IBM defines machine learning (ML) as 'a branch of artificial intelligence (AI) that focuses on the use of data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.' [1]

## How it differs from traditional programming

Google's Machine Learning Crash Course explains that traditional software is written as explicit rules, while ML systems learn patterns from examples (training data) and then make predictions on new, unseen data. [2]

## Three common types

- Supervised learning — the model learns from labelled examples (e.g. emails marked 'spam' or 'not spam'). [2]
- Unsupervised learning — the model finds structure in unlabelled data (e.g. grouping similar customers). [2]
- Reinforcement learning — an agent learns by trial and error, receiving rewards for good actions. [1]

## Where it fits inside AI

The U.S. National Institute of Standards and Technology (NIST), in its AI Risk Management Framework, treats machine learning as one of the main technical approaches used to build AI systems, alongside other techniques such as rule-based systems. [3]

> **A simple mental model** _(tip)_
>
> Traditional code: rules + data → answers. Machine learning: data + answers → rules (a trained model). [2]

## References (clickable)

- [1] IBM — What is machine learning?: https://www.ibm.com/topics/machine-learning
- [2] Google — Machine Learning Crash Course, Introduction to ML: https://developers.google.com/machine-learning/crash-course/ml-intro
- [3] NIST — AI Risk Management Framework (AI RMF 1.0): https://www.nist.gov/itl/ai-risk-management-framework

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