For someone outside the IT industry, all these terms can be a bit confusing. After all, we have artificial intelligence, machine learning, deep learning, big data, business intelligence, and many more power words that have been revolving around the IT world for several years now. Today, however, we are going to focus strictly on the difference between machine learning and deep learning. Yes, we are going to have a little competition with two contestants: deep learning vs. machine learning. It’s time to find out what these two technologies are about.

machine learning, AI

The most straightforward way to explain the difference between machine learning and deep learning is to compare these two technologies and their real-life applications. And this is exactly what we are going to do, but first, a short introduction:

Both deep learning and machine learning are subsets of artificial intelligence. The term AI is commonly used to describe all the technologies, algorithms, and programs capable of working without human assistance.

The next crucial thing you have to know is that deep learning and machine learning are, in essence, the same technology. Yes, it’s not a mistake: Deep learning IS machine learning. However, deep learning is considered an evolution, a more advanced version of machine learning. It uses a programmable neural network that enables algorithms to make accurate decisions and even ‘think’ in an abstractive way without any assistance or supervision from human scientists.

With this introduction done, we can switch to a more detailed comparison.

How does machine learning work?

Machine learning is all about creating specific algorithms that can improve themselves without human intervention, based on the data they possess and analyze.

The machine learning algorithms parse data, learn from it in a similar way humans learn–based on experience (previous knowledge), and then apply what they’ve learned to produce the desired outcome.

Machine learning can be used for almost all sorts of automated tasks spanning across multiple industries, from data security firms that fight malware and spam, through pharmaceutical companies looking for a perfect drug for migraine, up to finance professionals who want alert for favorable trades or possible credit frauds. The ML algorithms are programmed to constantly learn and, thus, become more and more accurate.

However, we have to state that machine learning algorithms require structured/labeled data, so they are not suitable for solving complex queries that involve a massive amount of data. In other words, at the beginning of each project, you have to teach them how to work properly. That’s why ML algorithms need structured data–it allows them to analyze it and then draw conclusions.

If you’re struggling with a more complicated task, you’d be better off with deep learning (remember? It’s improved machine learning), but more about that in a minute.

You may also find it interesting – Machine Learning in Applications.

deep learning

What is ML used for?

When it comes to machine learning, we always begin with a specific algorithm and data that it can base on. Algorithms are designed to achieve specific goals or undertake a specific action. Maybe a couple of examples:

  • Fraud detection: ML algorithms can scan for anomalous activity on the banking accounts and detect possible frauds.
  • Designing new drugs: ML algorithms can scan thousands of possible chemical combinations in order to devise efficient medicine for a specific disease.
  • Product recommendations: Every time you turn on Netflix or go to your favorite online store, you see personalized recommendations based on your previous choices and searches. The machine learning algorithms associate your preferences with other users who have a similar taste, and, based on that, provide you with accurate recommendations. It’s also a machine learning algorithm’s job.
  • Hiring people: Let’s say the company has a perfect sales rep profile. The ML algorithm can scan received resumes and match them with the previously created profile. As a result, the company obtains resumes, which are closest to the perfect sales rep profile, and can hire the best candidate.

Naturally, there are many more examples of machine learning algorithm applications in the real world. For instance, it’s commonly used in computer vision, data analysis, image processing, image classification, and many more interesting fields.

The thing is, the machine learning algorithms need data and initial training to operate. In the fraud detection example, this data is banking accounts. In the last example, it’s the resumes that the company has received during the employing process.

To sum up, although machine learning is a very sophisticated technology, it has limitations. That’s why AI specialists had to come up with another solution, more complex, capable of handling more advanced tasks and projects. And they did. That’s how deep learning has come into existence.

If you are interested in ML technology, read our article on automated machine learning.


What is the difference between deep learning and machine learning?

Now, we can switch to deep learning and find out something more about this amazing technology. But first things first. Deep learning is actually a subset of machine learning. It’s a similar technology; it functions in a similar way but has much greater capabilities.

As you know from the previous part of this article, the machine learning algorithms need to be taught what they should do and how. They need guidance. That previous training is essential, and the better the training is, the better results you obtain. This is not the case when it comes to deep learning.

In reference to deep learning, an algorithm can determine all on its own if a prediction (or another outcome) is accurate, correct or not. It happens through its own neural network. And now is an excellent time to explain what the neural network is.


It’s a complex, multi-layered model that structures algorithms in layers. The idea behind the artificial neural network is to create a structure that functions similarly to the human brain–can make decisions on its own. The objective of neural networks and deep learning is to capture non-linear patterns in data (this is where machine learning loses) by adding more layers of parameters to the model. More layers permit higher levels of abstraction and improved predictions from input data.

The main advantage of deep learning is that it doesn’t necessarily need structured data to analyze or classify it. In this case, the input data is sent through (processed) different levels of neural networks, and each network hierarchically determines the specific features of input data. After processing, the system finds the answer. All on its own!

deep learning, hand, phone

It’s a bit easier to show it on an example: One of many niches where deep learning plays a significant role is the motor industry. Automotive researchers and engineers use deep learning to build systems that can automatically detect objects in front of a car, such as stop signs, traffic lights, and even pets and pedestrians. One of the companies developing such a system is Volvo, known for years for taking care of safety.

They have developed a system called CWAB[1] (Collision Warning with Full Auto Brake). This system reacts when a pedestrian walks out in front of a car. If the driver does not take any action, CWAB will instantly activate the car’s full braking power.

However, there is a price to pay. The deep learning algorithms require much more data than typical ML applications and are much more difficult to build. The reason for this is that deep learning networks can identify different elements in neural network layers only when more than a million data points interact[2].

Deep learning vs. machine learning–the major difference

Although these two technologies are similar, there are many differences, and there’s one crucial, among them.

Machine learning algorithms almost always require structured/labeled data and previous extended training. On the other hand, deep learning uses artificial neural networks to make decisions and analyze input data, almost entirely without human assistance or intervention.

Naturally, this does not mean that deep learning is a flawless technology. No such thing exists, and the mistakes simply can occur. But when they do, the deep learning algorithms can learn from them and improve themselves.

numbers, technology

Machine learning or deep learning–which is best?

At first, you might think that deep learning is better, simply because it’s more advanced. But, as you already know, it also requires more input data. It’s a massive simplification, but you don’t need an advanced scientific calculator to calculate simple primary school mathematical operation. Your ordinary pocket calculator is fully sufficient. The same thing is with the machine and deep learning.

You should rather think of these two technologies as complementary solutions. However, we are far from saying that machine learning is worse in any way. It’s a technology designed to handle more straightforward tasks, and for that, it’s perfect!

On the other hand, when you start working with massive amounts of data, when problems you want to solve become more and more complex–this is where deep learning comes into play.

However, if you are at the very beginning of your AI journey, most likely, you don’t need deep learning at this stage. Although we say machine learning is more straightforward, it’s still an impressive technology, capable of dealing with many problems!

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