What Is Machine Learning? Definition, Types, and Examples | Coursera

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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.
Machine learning is a subfield of artificial intelligence that uses algorithms and data to create models capable of performing tasks without human intervention.
Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models capable of performing tasks such as categorizing images, analyzing data, or predicting price fluctuations.
Algorithms are refined using past data sets to make predictions and categorizations when confronted with new data.
You can learn more about how machine learning works, its different types, and how it is used in the real world.
Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models capable of performing 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.
Read on to learn more about the mechanics, types, and real-world applications of this technology, as well as the benefits and dangers that machine learning poses. Afterward, if you'd like to build your own machine learning skills, you might consider enrolling in DeepLearning.AI and Stanford's Machine Learning Specialization.
Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models 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.
Read more: Machine Learning vs. AI: Differences, Uses, and Benefits
Machine learning is typically the most mainstream type of AI technology in use 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, to improve overall vehicle safety.
Learn more about the real-world applications of machine learning in the following lecture from Stanford and DeepLearning.AI's Machine Learning Specialization.
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.
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
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 intervention – 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.
In supervised 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 it 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 learning is often used to create machine learning models used for prediction and classification purposes.
Unsupervised learning uses unlabeled data sets to train algorithms. In this process, the