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Machine Learning and Deep Learning - is there a difference?

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Machine learning and deep learning have really taken the spotlight away from the earlier buzzword, big data. The repetitive use of these terms to define everything that has some sort of automation going underneath has blown these technologies to great proportions and currently it is hard even to decipher what is what. If you know the right questions you may as well get the right answers about machine learning and deep learning. Otherwise they can just stay as interchangeable buzzwords.

To kill the confusion

The most important thing to be learnt about the relationship between these two technological marvels is that deep learning is an evolution of machine learning. It is not a rival but a successor of machine learning in terms of data parsing and decision augmentation. We use two different terms to identify them because both the forms are in regular use.

What is machine learning?

“Machine learning is the process of creating algorithms that can parse data, learn from the data, and use what they have learnt to perform a certain task.”


Machine learning is currently being used across a wide range of industries. From fraud detection algorithms used by banks and financial institutes, to algorithms to predict which student is likely to dropout, to models to find songs that you are likely to enjoy, everything has machine learning under the hood.


There are different types of machine learning like supervised learning, unsupervised learning, and reinforcement learning. Each of these types use different models to train the algorithms. The most common ones are regression models, clustering models, classification models, and dimensionality reduction models.


A classic example of machine learning in action would be classification of customers based on their age, gender, purchase history, credit score, while factoring in the time of the year and festivities.

How is deep learning different?

Deep learning has a similar goal to that of machine learning, obviously, since we have already clarified that deep learning is an extension on machine learning. But there is a difference in approach that distinguishes deep learning. Let us find out what deep learning is and why it is called so.


At the root of deep learning lies artificial neural networks. Artificial neural networks emulate the working principles of the neurons in the human nervous system. An artificial neural network consists of multiple neurons or nodes that are interconnected. These neural networks are used to recognize patterns in data to push the algorithms towards a certain action. Usually it requires many layers of neural networks to create a functional, intelligent analytical system. These multiple layers together are referred to as a deep neural network, The process of training the algorithms involving deep neural networks is called deep learning.


So, as you can see, deep learning achieves the same goal of learning from data but through a very different approach.

The origins

The idea of machine learning was coined in the late 1950s and the first electronic neural neuron was created around the same time. Both the approaches have contributed significantly to the ideation and realization of artificial intelligence as we see it today. Deep learning joined the action in the real world a little later than machine learning primarily because it required more data and more advanced computational powers. These theories were waiting for technology to catch up, and now it has.

Factors that distinguish them

While both these formulators of machine intelligence are dependent on data, deep learning algorithms need more data to be functional than traditional machine learning algorithms.


The technologies like natural language processing, speech recognition, and computer vision could not have achieved the level of accuracy they currently feature without deep learning.

The business end

Both machine learning and deep learning are like rocket engines that need to be fueled by data to take off. Businesses that can come up with data strategies to fuel their efforts in deep learning can achieve great heights, of course, if they have a good plan for action. A lot of businesses are in fact investing in deep learning despite the risk involved. The tech giants are turning to deep learning to optimize their services, take Facebook’s face recognition engine for instance. The use of machine learning to automate repetitive tasks is well established across different industries. 


The field of artificial intelligence is full of opportunities for those who have the right skill set along with the right attitude. A machine learning online course on top of relevant coding and computing skills can help you start a career in machine learning. Getting into deep learning requires a few more steps along the same line.

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