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Machine Learning vs. Deep Learning: What’s the Difference?

According to LinkedIn, between 2018–2024, the global machine learning market is expected to expand at 42.08% CAGR. Today, machine learning is applicable in almost every industry, from healthcare, finance, and entertainment to retail, manufacturing, and more.

The adoption of machine learning has become essential for boosting revenue, cutting costs, and automating operations.

Machine Learning vs. Deep Learning

While machine learning and deep learning are incredibly similar and nearly interchangeable, they are not quite the same. So how are they different? 

While deep learning is a type of machine learning, machine learning is certainly not deep learning. For example, a square is a rectangle, but a rectangle is not a square. Simple and easy enough, right?

Maybe. Machine learning and deep learning are both forms of artificial intelligence. Machine learning lets computers learn by themselves. Deeper learning is an algorithm that tries to learn the same way the human brain does by using the information to create more profound meanings of data. 

Machine Learning in Action

Machine learning can analyze data, learn from it, and then make predictions. Machine learning is much like any machine’s work: it is trained to do something and then perform that task. It learns logic and then creates a solution. 

Machine learning is able to do this by utilizing different algorithms such as:

Naive Bayes — A group of algorithms that do the same thing, which is that every feature being classified is independent of the value of any other feature. This work likes predicting emotions in photos.

Logistic Regression — Predicts the value of categorical outcomes in limited numbers of values.

Classification —Separates data into different groupings.

Random Forest and Decision Trees — A group of simple tree predictors that can produce a response, much like Youtube predicting other videos you might like to watch based on your history.

Linear Regression — Predicts the value of categorical outcomes with endless outcomes. 

Machine learning also utilizes supervised learning and unsupervised learning. Supervised learning means a person will input data and the solution while letting the machine predict the relationship between input and explanation. Machine learning is heavily used for mathematics. 

But unsupervised learning is the input of random data for a situation and then asking the computer to figure out both the relationship and the solution. Machine learning allows people to do away with endless coding or taking on the task of analyzing all the data by themself to find answers or logic. This is incredibly useful to make working more accessible and not waste time.

What’s Deep Learning 

But now what is deep learning? It’s a bit more of an intense version of machine learning. The short answer is that it just crunches more data. They’re powerful algorithms that require a lot more data. But these powerful algorithms require equally powerful machines that machine learning doesn’t need. This means deeper learning will require a bit more money to run. Deep learning can take on more complex tasks, like matrix multiplications. It’s also much better at facial recognition. Machine learning is great and can get the job done, but deep learning will always be able to take you to a deeper level.

Dive into Deep and Machine Learning

Explore the world of AI with Udacity’s plethora of Deep and Machine Learning Nanodegrees — Deep Learning, Machine Learning Engineer for Microsoft Azure, Intro to Machine Learning with TensorFlow, Intro to Machine Learning with PyTorch, and AWS Machine Learning Engineer

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Jennifer Shalamanov
Jennifer Shalamanov
Jennifer is a content writer at Udacity with over 10 years of content creation and marketing communications experience in the tech, e-commerce and online learning spaces. When she’s not working to inform, engage and inspire readers, she’s probably drinking too many lattes and scouring fashion blogs.