Deep Learning vs. Machine Learning: Beginner’s Guide

Even if you’re not involved in data science, you’ve probably heard the terms artificial intelligence (AI), machine learning, and deep learning thrown around in recent years. Sometimes, they’re even used interchangeably. While related, each of these terms has its distinct meaning, and they're more than just buzzwords used to describe self-driving cars.

In broad terms, deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence. You can think of them as a series of overlapping concentric circles, with AI occupying the largest, followed by machine learning, then deep learning. In other words, deep learning is AI, but AI is not deep learning. 

Read on to learn more about AI, machine learning, and deep learning, including how they're related and differ. 

Deep learning vs. machine learning

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 to mimic the human brain's learning process.

Take a look at these key differences.

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. 

We need machine learning for a machine or program to improve independently without further input from human programmers.

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 add more features and possibilities.

What is machine learning?

Machine learning refers to the study of computer systems that learn and adapt automatically from experience without humans explicitly programming them.

With simple AI, a programmer can tell a machine how to respond to various instructions by hand-coding each “decision.” With machine learning models, computer scientists can “train” a machine by feeding it large amounts of data. The machine follows a set of rules—called an algorithm—to analyse and draw inferences from the data. The more data the machine parses, the better it can become at performing a task or making a decision.

For example, the music streaming service Spotify learns your music preferences to offer new suggestions. Each time you indicate that you like a song by listening through to the end or adding it to your library, the service updates its algorithms to feed you more accurate recommendations. Netflix and Amazon use similar machine learning algorithms to offer personalised recommendations.

What is deep learning?

Where machine learning algorithms generally need human correction when they get something wrong, deep learning algorithms can improve their outcomes through repetition without human intervention. A machine learning algorithm can learn from relatively small data sets, but a deep learning algorithm requires big data sets that might include diverse and unstructured data.

Think of deep learning as an evolution of machine learning. Deep learning is a machine learning technique that layers algorithms and computing units—or neurons—into an artificial neural network. These deep neural networks are inspired by the structure of the human brain. Data passes through this web of interconnected algorithms non-linearly, much like how our brains process information. 

What’s the big deal with big data?

“Big data” refers to data sets too big for traditional relational databases and data processing software to manage. Businesses generate unprecedented amounts of data each day, and deep learning is one way to derive value from that data. 

Getting started in AI and machine learning

AI, machine learning, and deep learning are all connected. Deep learning is the most advanced and requires large amounts of data to learn and make complex, non-linear correlations. Machine learning is simpler, requires less data, and makes linear correlations.

Continue exploring the technologies with programmes available on Coursera. For example, if this introduction to AI, deep learning, and machine learning has piqued your interest, consider taking AI for Everyone, a course designed to teach AI basics to students from a non-technical background.

For more advanced knowledge, start with Andrew Ng’s Machine Learning Specialisation for a broad introduction to machine learning concepts. Next, build and train artificial neural networks in the Deep Learning Specialisation.

When you’re ready, start building the skills needed for an entry-level role as a data scientist with the IBM Data Science Professional Certificate.

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