What Is Machine Learning? Definition, Types, and Examples (2025)

Written by Coursera Staff • Updated on

Machine learning is a common type of artificial intelligence. Learn more about this exciting technology, how it works, and the major types powering the services and applications we rely on every day.

What Is Machine Learning? Definition, Types, and Examples (1)

Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models that enable machines to perform tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuations.

Today, machine learning is one of the most common forms of artificial intelligence and often powers many of the digital goods and services we use every day.

In this article, you’ll learn more about what machine learning is, including how it works, different types of it, and how it's actually used in the real world. We’ll take a look at the benefits and dangers that machine learning poses, and in the end, you’ll find some cost-effective, flexible courses that can help you learn even more about machine learning.

Beginner-friendly machine learning courses

Interested in learning more about machine learning but aren't sure where to start? Consider enrolling in one of these beginner-friendly machine learning courses on Coursera today:

In Open.AI and Stanford's Machine Learning Specialization, you'll master fundamental AI concepts and develop practical machine-learning skills in as little as two months.

The University of London's Machine Learning for All course will introduce you to the basics of how machine learning works and guide you through training a machine learning model with a data set on a non-programming-based platform.

What Is Machine Learning? Definition, Types, and Examples (2)

Machine learning definition

Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models that are capable of predicting outcomes and classifying information without human intervention. Machine learning is used today for a wide range of commercial purposes, including suggesting products to consumers based on their past purchases, predicting stock market fluctuations, and translating text from one language to another.

In common usage, the terms “machine learning” and “artificial intelligence” are often used interchangeably with one another due to the prevalence of machine learning for AI purposes in the world today. But, the two terms are meaningfully distinct. While AI refers to the general attempt to create machines capable of human-like cognitive abilities, machine learning specifically refers to the use of algorithms and data sets to do so.

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

Examples and use cases

Machine learning is typically the most mainstream type of AI technology in use around the world today. Some of the most common examples of machine learning that you may have interacted with in your day-to-day life include:

  • Recommendation engines that suggest products, songs, or television shows to you, such as those found on Amazon, Spotify, or Netflix.

  • Speech recognition software that allows you to convert voice memos into text.

  • A bank’s fraud detection services automatically flag suspicious transactions.

  • Self-driving cars and driver assistance features, such as blind-spot detection and automatic stopping, improve overall vehicle safety.

Learn more about the real-world applications of machine learning in this lecture from Stanford and DeepLearning.AI's Machine Learning Specialization:

Read more: 9 Real-Life Machine Learning Examples

How does machine learning work?

Machine learning is both simple and complex.

At its core, the method simply uses algorithms – essentially lists of rules – adjusted and refined using past data sets to make predictions and categorizations when confronted with new data. For example, a machine learning algorithm may be “trained” on a data set consisting of thousands of images of flowers that are labeled with each of their different flower types so that it can then correctly identify a flower in a new photograph based on the differentiating characteristics it learned from other pictures.

To ensure such algorithms work effectively, however, they must typically be refined many times until they accumulate a comprehensive list of instructions that allow them to function correctly. Algorithms that have been trained sufficiently eventually become “machine learning models,” which are essentially algorithms that have been trained to perform specific tasks like sorting images, predicting housing prices, or making chess moves. In some cases, algorithms are layered on top of each other to create complex networks that allow them to do increasingly complex, nuanced tasks like generating text and powering chatbots via a method known as “deep learning.”

As a result, although the general principles underlying machine learning are relatively straightforward, the models that are produced at the end of the process can be very elaborate and complex.

Machine learning vs. deep learning

As you’re exploring machine learning, you’ll likely come across the term “deep learning.” Although the two terms are interrelated, they're also distinct from one another.

Machine learning refers to the general use of algorithms and data to create autonomous or semi-autonomous machines. Deep learning, meanwhile, is a subset of machine learning that layers algorithms into “neural networks” that somewhat resemble the human brain so that machines can perform increasingly complex tasks.

Read more: Deep Learning vs. Machine Learning: Beginner’s Guide

What Is Machine Learning? Definition, Types, and Examples (3)

Types of machine learning

Several different types of machine learning power the many different digital goods and services we use every day. While each of these different types attempts to accomplish similar goals – to create machines and applications that can act without human oversight – the precise methods they use differ somewhat.

To help you get a better idea of how these types differ from one another, here’s an overview of the four different types of machine learning primarily in use today.

1. Supervised machine learning

In supervised machine learning, algorithms are trained on labeled data sets that include tags describing each piece of data. In other words, the algorithms are fed data that includes an “answer key” describing how the data should be interpreted. For example, an algorithm may be fed images of flowers that include tags for each flower type so that it will be able to identify the flower better again when fed a new photograph.

Supervised machine learning is often used to create machine learning models used for prediction and classification purposes.

2. Unsupervised machine learning

Unsupervised machine learning uses unlabeled data sets to train algorithms. In this process, the algorithm is fed data that doesn't include tags, which requires it to uncover patterns on its own without any outside guidance. For instance, an algorithm may be fed a large amount of unlabeled user data culled from a social media site in order to identify behavioral trends on the platform.

Unsupervised machine learning is often used by researchers and data scientists to identify patterns within large, unlabeled data sets quickly and efficiently.

3. Semi-supervised machine learning

Semi-supervised machine learning uses both unlabeled and labeled data sets to train algorithms. Generally, during semi-supervised machine learning, algorithms are first fed a small amount of labeled data to help direct their development and then fed much larger quantities of unlabeled data to complete the model. For example, an algorithm may be fed a smaller quantity of labeled speech data and then trained on a much larger set of unlabeled speech data in order to create a machine learning model capable of speech recognition.

Semi-supervised machine learning is often employed to train algorithms for classification and prediction purposes in the event that large volumes of labeled data is unavailable.

4. Reinforcement learning

Reinforcement learning uses trial and error to train algorithms and create models. During the training process, algorithms operate in specific environments and then are provided with feedback following each outcome. Much like how a child learns, the algorithm slowly begins to acquire an understanding of its environment and begins to optimize actions to achieve particular outcomes. For instance, an algorithm may be optimized by playing successive games of chess, which allows it to learn from its past successes and failures playing each game.

Reinforcement learning is often used to create algorithms that must effectively make sequences of decisions or actions to achieve their aims, such as playing a game or summarizing an entire text.

Read more: 3 Types of Machine Learning You Should Know

Machine learning benefits and risks

Machine learning is already transforming much of our world for the better. Today, the method is used to construct models capable of identifying cancer growths in medical scans, detecting fraudulent transactions, and even helping people learn languages. But, as with any new society-transforming technology, there are also potential dangers to know about.

At a glance, here are some of the major benefits and potential drawbacks of machine learning:

BenefitDangers
Decreased operational costs: AI and machine learning may help businesses to automate some of its jobs, causing overall operational costs to decrease.Job layoffs: as some jobs are automated, workers in the impacted field will likely face layoffs that could force them to switch to a new career or risk long-term unemployment.
Improved operational efficiency and accuracy: Machine learning models are able to perform certain narrow tasks with extreme efficiency and accuracy, ensuring that some tasks are completed to a high degree in a timely manner.Lack of human element: Models that are tasked with doing a very narrow task may also miss many of the “human” aspects of the job that are important to it but potentially overlooked by developers.
Improved insights: Machine learning has the potential to quickly identify trends and patterns in large amounts of data that would be time consuming for humans. These insights can equip businesses, researchers, and society as a whole with new knowledge that has the potential to help them achieve their overall goals.Ingrained biases: Just like the humans that create them, machine learning models can exhibit bias due to the occasionally skewed data sets that they’re trained on.

Have career questions? We have answers.

Subscribe to Coursera Career Chat on LinkedIn to receive our weekly, bite-sized newsletter for more work insights, tips, and updates from our in-house team.

What Is Machine Learning? Definition, Types, and Examples (4)

Learn more with Coursera

AI and machine learning are quickly changing how we live and work in the world today. As a result, whether you’re looking to pursue a career in artificial intelligence or are simply interested in learning more about the field, you may benefit from taking a flexible, cost-effective machine learning course on Coursera.

In DeepLearning.AI and Stanford’s Machine Learning Specialization, you’ll master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.

In IBM’s Machine Learning Professional Certificate, you’ll master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles, including how to use supervised and unsupervised learning to build models for a wide range of real-world purposes.

Updated on

Written by:

C

Coursera Staff

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

What Is Machine Learning? Definition, Types, and Examples (2025)

FAQs

What Is Machine Learning? Definition, Types, and Examples? ›

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.

What is machine learning and explain types and examples? ›

Machine learning is a subset of AI, which uses algorithms that learn from data to make predictions. These predictions can be generated through supervised learning, where algorithms learn patterns from existing data, or unsupervised learning, where they discover general patterns in data.

What is machine learning simple answer? ›

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

What is machine learning sample definition? ›

In machine learning, samples measure all variables in the data set and are divided into training, selection, and test samples. A sample contains one or more features and possibly a label. Samples can be labeled or unlabeled.

What is machine learning explained the simple way? ›

Answer: Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.

What are the major types of learning styles used in machine learning? ›

Supervised, Self-Supervised, Unsupervised, and Reinforcement Learning.

What is the goal of machine learning? ›

The Goals of Machine Learning.

(1) To make the computers smarter, more intelligent. The more direct objective in this aspect is to develop systems (programs) for specific practical learning tasks in application domains. (2) To dev elop computational models of human learning process and perform computer simulations.

What is machine learning in own words? ›

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. How does machine learning work?

What is the easiest way to explain machine learning? ›

In other words, machine learning involves computers finding insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

How do you explain machine learning in layman's terms? ›

It is a way of achieving artificial intelligence without having to specify all the rules and processes ahead of time. Machine-learning algorithms use historical data to predict new output values and make decisions based on that information.

What is the best example of machine learning? ›

Here are examples of machine learning at work in our daily life that provide value in many ways—some large and some small.
  1. Facial recognition. ...
  2. Product recommendations. ...
  3. Email automation and spam filtering. ...
  4. Financial accuracy. ...
  5. Social media optimization. ...
  6. Healthcare advancement. ...
  7. Mobile voice to text and predictive text.

What are the four basics of machine learning? ›

Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. The four basic types of ML are supervised learning, unsupervised learning, semisupervised learning and reinforcement learning.

What is machine learning for dummies? ›

the process of exploring and analyzing large amounts of data to discover patterns in that data. Algorithms are used to find relationships and patterns in the data, and then this information about the patterns is used to make forecasts and predictions.

What is the definition of machine learning and examples? ›

Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models that are capable of predicting outcomes and classifying information without human intervention.

What is the main idea of machine learning? ›

The goal of machine learning is to train machines to get better at tasks without explicit programming. To achieve this goal, several steps have to take place. First, data needs to be collected and prepared. Then, a training model, or algorithm, needs to be selected.

What is machine learning summary? ›

In general, machine learning is a field of artificial intelligence that is intended to explore constructs of algorithms that make it possible to understand autonomously, where it creates the possibility to recognize and extract patterns from a large volume of data, thus building a model of learning [43,44].

What are the basic data types in machine learning? ›

Data can come in many forms, but machine learning models rely on four primary data types. These include numerical data, categorical data, time series data, and text data.

What is the difference between AI and machine learning? ›

Differences between AI and ML

While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns.

What are three types of machine learning model monitoring? ›

3 types of machine learning models

They are: Descriptive - to help understand what happened in the past. Prescriptive - to automate business decisions and processes based on data. Predictive - to predict future business scenarios.

Is ChatGPT machine learning? ›

ChatGPT is an extrapolation of a class of machine learning Natural Language Processing models known as Large Language Model (LLMs). LLMs digest huge quantities of text data and infer relationships between words within the text.

Top Articles
Latest Posts
Recommended Articles
Article information

Author: Jamar Nader

Last Updated:

Views: 6040

Rating: 4.4 / 5 (55 voted)

Reviews: 94% of readers found this page helpful

Author information

Name: Jamar Nader

Birthday: 1995-02-28

Address: Apt. 536 6162 Reichel Greens, Port Zackaryside, CT 22682-9804

Phone: +9958384818317

Job: IT Representative

Hobby: Scrapbooking, Hiking, Hunting, Kite flying, Blacksmithing, Video gaming, Foraging

Introduction: My name is Jamar Nader, I am a fine, shiny, colorful, bright, nice, perfect, curious person who loves writing and wants to share my knowledge and understanding with you.