What Is Machine Learning? How It Works and Where It Is Used

Machine learning is the part of AI where computers learn patterns from examples instead of following rules a person wrote by hand. This guide explains how it works, how neural networks, natural language processing (NLP) and large language models (LLMs) fit inside it, where it is used, and what to check before you build with it.

What is machine learning?

Carnegie Mellon's Tom Mitchell frames the field around one question: "How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?" He gives a precise test as well: a machine learns with respect to a task T, a performance measure P and a type of experience E "if the system reliably improves its performance P at task T, following experience E."

In practice, that means you stop writing "if this, then that" rules for every case. You show a system many examples, let it find the pattern, and measure how well it does on cases it has not seen.

Key takeaways

• Machine learning learns patterns from data; the quality of the data limits the quality of the result.
• Neural networks are one family of ML models, and deep learning means neural networks with many layers.
• NLP applies ML to human language; large language models are the current most visible example.
• ML is widely used, but it fails in predictable ways: poor data, bias, drift and overconfident answers.
• Start from a specific problem and a way to measure success, not from a technique.

How does machine learning work?

• Define the task. What should the system predict or decide, and how will you measure it?
• Collect and prepare data. Gather examples, clean them, and set aside some to test on later.
• Train a model. The learning algorithm adjusts the model so it performs better on the training examples.
• Evaluate on unseen data. A model that only does well on what it was trained on has memorized, not learned.
• Deploy and monitor. Real-world data changes, so performance has to be watched after launch.

What are the main types of machine learning?

• Supervised learning: learn from examples that come with the right answer, such as emails labeled spam or not spam.
• Unsupervised learning: find structure in data that has no labels, such as grouping similar customers.
• Reinforcement learning: learn by trial and reward, such as a system improving at a game or a control task.

Where do neural networks fit in?

A neural network is a model built from layers of simple connected units. Each connection has a weight, and training adjusts those weights so the network's outputs get closer to the right answers. A network with many layers is what people call deep learning, which has its own guide: What Is Deep Learning?.

Where does NLP fit in?

Natural language processing is, in the words of our glossary, "the field of AI that enables computers to understand, interpret, and generate human language." Typical NLP tasks are classifying text, extracting information from it, translating, summarizing and answering questions. Modern NLP relies on machine learning, and on embeddings, which turn words and passages into numbers a model can compare. Our NLP fundamentals guide goes deeper.

Where do large language models fit in?

The Transformer architecture, introduced in 2017, replaced the "complex recurrent or convolutional neural networks in an encoder-decoder configuration" that dominated sequence tasks with a design based on attention. Large language models are built on that idea. As our glossary puts it, an LLM is "an AI model trained on massive text datasets that can understand and generate human-like text." They read and write tokens, which are pieces of text. LLMs are also the main engine of generative AI, covered in What Is Generative AI?.

How is machine learning used?

Mitchell noted back in 2006 that learning algorithms were already "routinely used in commercial systems for speech recognition, computer vision, and a variety of other tasks." Adoption has grown since: the Stanford AI Index reports that 78% of organizations said they used AI in 2024, up from 55% the year before. Common uses include:

• Recommendations and personalization: suggesting products, content or next steps.
• Fraud and anomaly detection: flagging transactions or events that look unusual.
• Forecasting and planning: predicting demand, workloads or maintenance needs.
• Vision: recognizing objects in images, such as quality inspection or medical imaging support.
• Speech and language: transcription, translation, search and customer support.
• Automation: deciding what should happen next in a process (see What Is Flow AI?).

Where does machine learning go wrong?

• Poor or unrepresentative data produces models that fail on the cases that matter.
• Bias in the data can be reproduced or amplified by the model.
• Drift: the world changes and a once-accurate model slowly stops being accurate.
• Opacity: some models cannot easily explain why they gave an answer.
• Privacy: training and using models can involve personal data.

These issues are the subject of our guide to ethical AI.

What should you check before building with machine learning?

• What exact decision or task is the model for, and what is the cost of a wrong answer?
• Do you have enough relevant, legally usable data, and who owns it?
• How will you measure success before launch, and monitor it afterwards?
• Is a simpler rule or report enough? Not every problem needs a model.
• Who reviews the outputs, and what happens when the model is uncertain?

Where Zunkiree Labs fits

Zunkiree Labs builds custom AI systems (including RAG pipelines, LLM integration and intelligent automation), data systems, custom software, and web and mobile applications. Our Orca is described plainly: Orca is Zunkiree Labs' intelligence and orchestration layer that sits above the CRM, email and marketing tools an organization already uses and coordinates agent workflows across them, rather than replacing those tools. The full list is on the services page, and the AI Development service covers custom AI systems in more detail.

Keep reading

• What Is AI? — the broader overview
• What Is Deep Learning? — how neural networks learn, and when they are the right tool
• What Is Generative AI? — how it works, where it helps, and where it fails
• Ethical AI — principles, risks and how organizations apply them
• Natural Language Processing Fundamentals — our guide to NLP
• How to Build a RAG Pipeline — a step-by-step engineering guide

Sources

• Tom M. Mitchell, The Discipline of Machine Learning (Carnegie Mellon University, July 2006)
• Vaswani et al., Attention Is All You Need (arXiv, 2017)
• Stanford HAI: 2025 AI Index Report