Deep Learning and how it is different from Machine Learning?

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Artificial Intelligence is a trending technology used by most businesses to automate their business processes.
Terms like Machine Learning and Deep Learning are interchangeably used for Artificial Intelligence. But these are two different terms. And the easiest way to understand the difference between Deep Learning and Machine Learning is by considering them the same.
Many Artificial Intelligence concepts narrow down to these two concepts Machine Learning and Deep Learning. But it is important to know the difference between these two terms to deduce some learning.
Those who know the concept of Deep Learning VS Machine Learning can observe the examples of the difference between Deep Learning and Machine learning everywhere.
This is the same as how Facebook knows the person in the picture, and; YouTube knows which video you would like to watch next.
These concepts are making a big impact on how we interact with different machines and apps how we consume entertainment and interact with other people on Social Media.
Let me help you to understand the difference between these two terms in layman’s language.

Deep Learning and how it is different from Machine Learning?

What is Machine Learning?

Since the evolution of computers, the computer was dependent upon human interventions to generate results even if the results are repetitive.

Data scientists and data analysts wonder whether computers can generate results without being programmed.

Machine Learning was not always this advanced. It started from data pattern recognition and the theory that computers can learn without being programmed to perform specific tasks.

Researchers and Data Scientists interested in Artificial Intelligence wanted to see if computers can learn from data whether computers can learn from previous computations  to produce repetitive results.

Machine Learning is a part of Artificial Intelligence and the study of computer algorithms that improves automatically through experience and use of data.

It uses Sample data, also known as “Training Data”, and previous Computations, to produce results by following several steps without being explicitly programmed.

Machine learning is now possible because of the varieties and volumes of data available, cheaper and faster computational processing, and affordable data storage.

Following are the things required to create good Machine Learning systems

Data Preparations Capabilities

For any computation, one needs to gather programmable data. Data should be in such a format that it can be used as an input to the system.


The Algorithm can be of two types Basic and advance. The Algorithm is a flow of data into a system to produce results.

Automation and iterative processes

The machine learning systems need an automation sub-part to understand the data and to produce repetitive in nature which undergoes iterative processes.


Machine learning systems should scale, analyzing volumes and varieties of data (BIG DATA) simultaneously.

Ensemble modeling

It is a process of applying different models to predict different outcomes after analyzing BIG DATA.

What is Deep Learning?

Deep Learning is a subset of Machine Learning. Artificial Intelligence has networks capable of unsupervised learning from unstructured or unprocessed data.

It is also known as Deep Neural Learning. Deep learning refers to computers or systems following human learning patterns from data and experiences.

It uses multiple layers to progressively extract output from raw data. It works similarly a human intelligence works to identify digits, letters, characters, and faces.

Deep learning refers to your computer system is capable of making intelligent decisions on its own.

Difference between Machine Learning and Deep Learning

Machine Learning VS Deep Learning can be deduced from the above discussions. This can be presented under different heads as follows;

Data: Small VS Tremendous

In Deep Learning huge amount of data is being processed in to machine learning. The data may be in any form, whether structured or unstructured. Commonly Deep Learning deals with complex problems. Deep learning tries to solve real-life problems with data insights.

Execution Time: Training VS Testing

To train a model in Machine Learning, the time incurred is comparatively less.

When you test a model in Machine Learning, the time required is more.

But, when it comes to deep learning, the time required for training a model is more as it deals with a large volume of data in one go. But the time needed for testing a model in a Deep Learning system would be less in comparison to Machine Learning.


In deep learning huge amount of data is processed, and the algorithm used requires complex mathematical calculations, so it requires powerful hardware than a simple Machine Learning system.

The Hardware used for deep learning is Graphical Processing Units (GPUs).

On the other hand, Machine Learning runs on lower-end machines.


Deep Learning and Machine Learning have various applications;

Deep Learning is applicable in health care in the diagnosis of tumor cells in humans.

Robotics which is an interdisciplinary field which detect nearby object and perform human Activity, which can be best seen in Self-Driving Cars. Using Deep Learning, cars identify the activity and distinguish the thing such as Traffic lights, buildings, roads, etc.

On the other hand machine learning is applicable in predicting stock market trends, Product recommendation on Flipkart, Amazon, and various other e-commerce portals, Email filtering etc.

The Machine adapts the pattern of the user’s interactions and uses the data for a recommendation.

Human Intervention/Feature Extraction

Deep Learning VS Machine Learning can be visible on the feature extraction pattern of their algorithms.

In Machine Learning human need to train the models, to feed the pattern of interactions manually.  Based on this features model, identify an object.

The programmer provides entire assistant.

On the other hand, in Deep Learning the system identifies an image and feeds it in deep learning algorithm on its own, and human don’t need to feed them manually.

The deep learning algorithm automatically generates a high-order feature related to the respective object and identifies the pattern.

Just a little assistant is required of the programmer.


From the above discussion, it is quite evident that Deep Learning is a subset of Machine Learning. Both are part of Artificial Intelligence Technology. Both these two systems use big data to predict the pattern of human Interactions.

Deep Learning can process a relatively tremendous amount of complex data in one go. Deep learning uses deep neural network that forms when one data node connects another node just like the human brain.

When connected, these nodes transmit signals, enabling them to distinguish various patterns and figures. Predict the next step based on experience. It can compute complex calculations without feeding a formula. It can generate recommendations to enhance entertainment experiences.

Derived from human intelligence and inspired by the human brain, deep learning can imitate the functioning of the human brain. So, it is applicable in various fields and for performing various tasks.

Deep learning has led basis to many new tech trends, and with infinite possibilities of its application, it is gaining popularity, it is taking over in our day-to-day experiences.

Deep Learning VS Machine Learning in the tech world today has made the business process even easier and better.

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