Difference between Deep Learning (DL) and Machine Learning (ML)

            To understand the complete AI concept, it is huge to know about it, as we discuss its inner sets of it in this post.   

As the above figure says that AI is the outer most layer and the ML and DL are the subsets of AI.

To know about Artificial Intelligence shortly here is the link What is artificial intelligence

The two terms are often thrown around in ways that can make them seem like interchangeable buzzwords, most evolving technologies, hence why it’s important to understand the differences.

  1. Machine Learning.
  2. Deep Learning.

Examples of Machine learning and deep learning are everywhere. Now, let us see how exactly the Machine learning and the deep learning differ?

What is Machine learning?

Here’s the basic definition,

Algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions”.

Machine Learning is the subset of Artificial Intelligence.

Machine learning is a lot of complex math and coding that, at the end of day, serves a mechanical function the same way a flashlight, a car, or a television does.

Ex: Think that you have a flashlight and you want it to turn it on when you say “it’s dark” .To have this task to be done you can include any phrase which has the word “dark” in it. This is because the Machine Learning algorithm will be designed in such a way that it mainly focuses on the specific key word dark”. It does catch any phrase that includes “dark” and it turn on the flashlight.

Machine learning fuels all sorts of automated tasks and spans across multiple industries.

It’s performing a function with the data given to it, and gets progressively better at that function.

An easy example of Machine learning algorithm is an on-demand music streaming service.

Spotify, is the best example for Machine learning, ever played one song and got some five random songs which you would have never discovered by yourself? That’s the beauty of this application named Spotify.

With the weekly releases, it gives you a list of around 30 songs, which you should listen to. It would directly make it as a playlist and send it to users. All of these songs are picked up by ML algorithms which analyze your activities and matches with your taste from the songs you have listened to in the past.

Some good examples which are in use of ML are:


ML is a fundamental part of this tech giant. From estimating the time to determining how far your cab is from your given location, everything is driven by ML.

It uses algorithms to determine all these effectively. It does these by analyzing the data from the previous trips and putting it in the present situation. Even the other branch of this giant i.e. UberEATS, does the same. It takes into account various factors like food preparation time to estimate the delivery time.

Siri & Cortana

These voice recognition systems are purely based on ML. Deep neural networks are also a part of these famous voice recognition systems. They are being trained in such a way that they can imitate human interactions in exactly the same manner. As the interactions proceed, these apps will learn to understand the skeleton and grammar of the language. With some famous slang, these can automatically get triggered with some pre-recorded responses from the system.

What is Deep learning?

In practical terms, Deep learning is just a subset of Machine learning,

In technical terms, Deep learning is Machine learning but, the capabilities of both are different.

Let us come back to the flashlight example, in machine learning it will work as mentioned in above scenario but, what if you add a phrase which doesn’t include “dark”?

Let’s say you have said “the lights are off”, “I can’t see”. The algorithms in deep learning which are capable to analyze the things and take the decisions on their own. A deep learning model is able to learn through its own method of computing – its own “brain”.

Machine learning v/s Deep learning (simple to understand)

        Basic machine learning models do become progressively better at whatever their function is. If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. But with a deep learning model, the algorithms can determine on their own if a prediction is accurate or not.

How does Deep learning work?

            A deep learning model is designed to continuously analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard machine learning models. Good examples for Deep learning are:

Self-driving cars

TESLA self-driving cars

        The tesla self-driving cars have the hardware components that could be helpful for the programs to interact with these components and make the decisions.

  • Eight surround cameras provide 360 degree visibility around the car at up to 250 meters of range.
  • Twelve updated ultrasonic sensors complement this vision, allowing for detection of both hard and soft objects at nearly twice the distance of the prior system.
  • Forward-facing radar with enhanced processing provides additional data about the world on a redundant wavelength, capable of seeing through heavy rain, fog, dust and even the car ahead.

Model S and Model X vehicles with this new hardware are already in production, and customers can purchase one today.


        Robots in general are the machines which have the ability to automate the tasks which are repeatedly to be executed in huge amount of production within a short span. Here the robots will be programmed into specific programs according to the requirements.

        Robotics in Artificial intelligence, there will be effective algorithms written and fed it to the machine so that it could be able to understand, analyze the tasks and to take the decisions of its own. If there are any inappropriate outcomes while in the processing any tasks, the machine itself will debug, with the help of artificial neural network  algorithms.

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