Quick answer
Machine learning is the part of AI in which software learns from examples (data) instead of following a rule someone wrote for every case. NIST's glossary says such systems "adapt and learn from data". This page sets NIST, Tom Mitchell's 1997 textbook page, Google and four vendors side by side, with the limit each one states.
Key takeaways
- Machine learning is the part of AI in which software learns from examples (data) instead of following a rule someone wrote for every case. NIST's glossary and Tom Mitchell's textbook page word it differently; the page sets both beside Google and four vendors.
- IBM: 'In short: all machine learning is AI, but not all AI is machine learning.'
- IBM, AWS, Google Cloud and NVIDIA each state a limit or condition that, in this site's reading, centers on the data the system learns from: its quality, its bias or its preparation.
- Sources name spam filtering, product recommendations, fraud detection and sales or inventory forecasts as examples of machine learning; whether a given product does this is for its seller to say.
- When a seller says 'it uses machine learning', this site suggests four questions: what data it learned from, how it is checked, what happens when it is wrong, and whether your data trains it (the last has no source here).
What is machine learning?
Machine learning is the part of AI in which software learns from examples (data) instead of following a rule someone wrote for every case.
The 9 sources read for this page word it differently. IBM contrasts it with rules a data scientist writes by hand, AWS says it works "without explicit instructions", Google Cloud says "without being explicitly programmed", and NIST says systems "adapt and learn from data". IBM's own comparison, from its spam example: writing out every criterion for spotting spam yourself, versus choosing an algorithm and giving the software an adequate dataset of sample emails. Three reference-style sources (Google also sells machine learning products):
- NIST (US standards agency, glossary): "The development and use of computer systems that adapt and learn from data with the goal of improving accuracy." Credited to NIST SP 800-55v1.
- Tom Mitchell (Carnegie Mellon page for his 1997 textbook; a sentence printed there, not a formal definition): "Machine Learning is the study of computer algorithms that improve automatically through experience."
- Google (developer course): "In basic terms, ML is the process of training a piece of software, called a model, to make useful predictions or generate content (like text, images, audio, or video) from data."
MIT Sloan's page says, of machine learning: "It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”" We did not check Samuel's own words.
What do four vendors say it is, and what limit does each state?
Each of the four vendors states a limit that, in this site's reading, is mostly about what the software was given to learn from, so ask a seller about the data first. That is our reading, not a finding: NVIDIA also names computing power, and IBM model selection. Cells are sentences from pages read on October 7, 2026, except NVIDIA's example cell, which gives items from its list of regression examples. The IBM spam sentence is IBM's own comparison. These companies sell products; nothing here ranks them.
| Vendor page | Its definition | An example it gives | A limit or condition it states |
|---|---|---|---|
| IBM | "Machine learning is the subset of artificial intelligence (AI) focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data." | "Consider a program that filters email spam: rules-based AI requires a data scientist to manually devise accurate, universal criteria for spam; machine learning requires only the selection of an appropriate algorithm and an adequate dataset of sample emails." | "Careful curation and preprocessing of training data, as well as appropriate model selection, are crucial steps in the MLOps pipeline." |
| AWS | "Machine learning is a type of artificial intelligence that performs data analysis tasks without explicit instructions." | "For example, a financial organization could train a machine learning system to classify fraudulent and genuine transactions." | "A machine learning model's performance depends on the data quality used for training." |
| Google Cloud | "Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data." | "Machine learning helps credit card companies and banks review vast amounts of transactional data to identify suspicious activity in real time." | "If a machine learning algorithm is fed a biased dataset, it'll deliver biased results." |
| NVIDIA | "Machine learning (ML) employs algorithms and statistical models that enable computer systems to find patterns in massive amounts of data, and then uses a model that recognizes those patterns to make predictions or descriptions on new data." | Items from NVIDIA's list of regression examples: "Predicting the amount of fraud" and "Predicting sales". | "Different algorithms are needed for different problems and tasks, and solving them depends as well on the quality of the input data and power of the computing resources." |
How is machine learning different from AI, deep learning and generative AI?
Sources describe machine learning as a subset of AI and deep learning as a subset of machine learning, while using the terms loosely. MIT Sloan: "When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously."
| Term | What a source says | Source |
|---|---|---|
| AI | "Full artificial intelligence involves machines that can perform abilities we associate with the minds of human beings and intelligent animals, such as perceiving, learning, and problem solving." | US Department of Energy |
| AI (broader use) | "The most elementary AI systems are a series of if-then-else statements, with rules and logic programmed explicitly by a data scientist." | IBM |
| Machine learning | "This makes machine learning a specific and narrow type of artificial intelligence." | US Department of Energy |
| Machine learning | "In short: all machine learning is AI, but not all AI is machine learning." | IBM |
| Deep learning | "Just as machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning." | Google Cloud |
| Generative AI | "Generative AI is a class of models that creates content from user input." (Google's course lists generative AI among the categories of ML systems.) | Google for Developers |
The first two rows use "AI" at different breadths (the Department of Energy by human-like abilities, IBM down to if-then-else programs). This site's clarification: ask a seller whether "AI" means software that learned from data or software that follows written rules.
Is a chat assistant that writes text AI or machine learning? No page read here names a specific product, so this is what follows for any assistant that creates content from what you type. Google's course calls generative AI "a class of models that creates content from user input" and lists generative AI among the categories of ML systems, and IBM says "all machine learning is AI". This site's inference from those two sources: such an assistant is both.
What are supervised, unsupervised and reinforcement learning?
Google's course describes three of its categories of machine learning in one sentence each.
- Supervised: "Supervised learning models can make predictions after seeing lots of data with the correct answers and then discovering the connections between the elements in the data that produce the correct answers." AWS adds: "However, labeling millions of unlabeled data sets is challenging."
- Unsupervised: "An unsupervised learning model aims to identify meaningful patterns in a dataset." The Department of Energy states: "The disadvantage of unsupervised learning is that the results may not be as accurate because of the lack of explicit labels."
- Reinforcement: "Reinforcement learning models make predictions by getting rewards or penalties based on actions performed within an environment."
Where will I meet machine learning without the name?
Sources name spam filtering, product recommendations, fraud flags and sales or inventory forecasts as examples. Whether a particular product of yours does this is for its seller to say.
| Where you meet it | A source that gives it |
|---|---|
| Spam filtering | Google's course: "For example, classification models are used to predict if an email is spam or if a photo contains a cat." NVIDIA also lists spam detection as a classification example. |
| Product recommendations | AWS: "For example, retailers recommend products to customers based on previous purchases, browsing history, and search patterns." |
| Fraud flags | MIT Sloan: "Fraud detection. Machines can analyze patterns, like how someone normally spends or where they normally shop, to identify potentially fraudulent credit card transactions, log-in attempts, or spam emails." |
| Sales or inventory forecasts | AWS: "For example, predictive analytics can anticipate inventory needs and optimize stock levels to reduce overhead costs." NVIDIA lists predicting sales as a regression example. |
What should I ask a seller who says "it uses machine learning"?
Ask four questions about the data and the checking, not about the algorithm. These are this site's suggestions; the fourth has no source.
- What data did it learn from, and is it like mine? Google Cloud says a biased dataset gives biased results, and NVIDIA says solving problems depends on the quality of the input data. Ask whether it resembles your customers and products.
- How is it checked, and by whom? IBM says validation after training is necessary to ensure a model generalizes well. Ask what was validated and ask to see it in writing.
- What happens when it is wrong? The Department of Energy says machine learning algorithms are not a silver bullet. Ask what a given product does after a wrong flag: a human review, an undo, a log.
- Does my data train it? The four vendor pages we read do not address a vendor's policy on your data. Get the answer in the contract or data-use terms.
Do I need to know this, and how does this site use it?
You need the idea, not the mathematics: ask what a tool learned from, and how someone knows it still works.
This site's own research on public data is here: how many small businesses use AI, by size and industry. For where AI tools fit in a small business, see the practical guide to AI automation.
Next step: send one seller the four questions in writing and keep the answers with the contract.
How was this page made?
Every quoted sentence comes from nine pages opened October 7, 2026: NIST, Carnegie Mellon (Tom Mitchell's textbook), Google's course, MIT Sloan, the Department of Energy, and IBM, AWS, Google Cloud and NVIDIA. The vendors' statements are theirs, not tested here. Drafted with Claude, an AI model made by Anthropic, and checked against each cited page. No Anthropic page is cited and no Claude product is described.
FAQ
What is the difference between AI and machine learning?
IBM says: 'In short: all machine learning is AI, but not all AI is machine learning.' The US Department of Energy says: 'This makes machine learning a specific and narrow type of artificial intelligence.'
Does machine learning need labeled data?
Some kinds do. Google's course says supervised learning models learn from lots of data with the correct answers, and AWS says: 'However, labeling millions of unlabeled data sets is challenging.'
Can a machine learning system be wrong?
Yes. The Department of Energy says: 'Machine learning algorithms are not a silver bullet. The development of machine learning systems is susceptible to human error and biases and requires the same careful design as software engineering.'