Deep Learning vs. Machine Learning: A Beginner’s Guide

Written by Coursera Staff • Updated on Dec 16, 2025

Deep learning is machine learning, and machine learning is artificial intelligence. But how do they fit together (and how do you get started learning)?

[Featured Image] A person works on a laptop at a living room table.

Key takeaways

While machine learning and deep learning are both types of AI, machine learning is a subset of AI, and deep learning is a subset of machine learning.

Machine learning models require human intervention when they get something wrong, whereas deep learning models can learn from their own mistakes.

While deep learning models require large amounts of data for training, you can train machine learning models on smaller data sets.

You can think of deep learning as an advanced form of machine learning, where artificial neural networks parse information similar to a human brain.

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What is deep learning vs. machine learning?

Thanks to pop culture depictions from 2001: A Space Odyssey to The Terminator, many of us have some conception of AI. Oxford Languages defines AI as “the theory and development of computer systems able to perform tasks that normally require human intelligence.” Britannica offers a similar definition: “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”

Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to mimic the learning process of the human brain.

Take a look at these key differences before we dive in further.

[Diagram] A venn diagram on a blue background showing how deep learning, machine learning, and AI are nested.

Machine learning	Deep learning
A subset of AI	A subset of machine learning
Can train on smaller data sets	Requires large amounts of data
Requires more human intervention to correct and learn	Learns on its own from the environment and past mistakes
Shorter training and lower accuracy	Longer training and higher accuracy
Makes simple, linear correlations	Makes non-linear, complex correlations
Can train on a CPU (central processing unit)	Needs a specialized GPU (graphics processing unit) to train

What is artificial intelligence (AI)?

At its most basic level, the field of artificial intelligence uses computer science and data to enable problem-solving in machines.

While we don’t yet have human-like robots trying to take over the world, we do have examples of AI all around us. These could be as simple as a computer program that can play chess, or as complex as an algorithm that can predict the RNA structure of a virus to help develop vaccines.

For a machine or program to improve on its own without further input from human programmers, we need machine learning.

Deep Blue, the chess-playing computer

Before the development of machine learning, artificially intelligent machines or programs had to be programmed to respond to a limited set of inputs. Deep Blue, a chess-playing computer that beat a world chess champion in 1997, could “decide” its next move based on an extensive library of possible moves and outcomes. But the system was purely reactive. For Deep Blue to improve at playing chess, programmers had to go in and add more features and possibilities.