The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning.
Machine learning is an application of AI that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions.
Deep learning is a subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.

Most people don’t realize that machine learning, which is a type of artificial intelligence (AI), was born in the 1950s. Arthur Samuel wrote the first computer learning program in 1959, in which an IBM computer got better at the game of checkers the longer it played. Fast-forward to today, when AI isn’t just cutting-edge technology; it can lead to high-paying and exciting jobs. Machine learning engineers are in high demand because, as upsaily MLE Tomasz Dudek says, neither data scientists nor software engineers have precisely the skills needed for the field of machine learning. Companies need professionals who are fluent in both of those fields yet can do what neither data scientists nor software engineers can. That person is a machine learning engineer. 

The terms “Artificial Intelligence,” “Machine Learning” and “Deep Learning” are often thrown about interchangeably, but if you’re considering a career in AI, it’s important to know how they’re different. According to the Oxford Living Dictionaries, artificial intelligence is “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” Although they might be called “smart,” some AI computer systems don’t learn on their own; that’s where machine learning and deep learning come in. 

What is Machine Learning?

With machine learning, computer systems are programmed to learn from data that is input without being continually reprogrammed. In other words, they continuously improve their performance on a task—for example, playing a game—without additional help from a human. Machine learning is being used in a wide range of fields: art, science, finance, healthcare—you name it. And there are different ways of getting machines to learn. Some are simple, such as a basic decision tree, and some are much more complex, involving multiple layers of artificial neural networks. The latter happens in deep learning.

What Is Deep Learning?

Some consider deep learning to be the next frontier of machine learning, the cutting edge of the cutting edge. You may already have experienced the results of an in-depth deep learning program without even realizing it! If you’ve ever watched Netflix, you’ve probably seen its recommendations for what to watch. And some streaming-music services choose songs based on what you’ve listened to in the past or songs you’ve given the thumbs-up to or hit the “like” button for. Both of those capabilities are based on deep learning. Google’s voice recognition and image recognition algorithms also use deep learning.