What is the difference between Machine Learning and Deep Learning

When we talk about data science or artificial intelligence, the two very common terminologies that come into account are MACHINE LEARNING and DEEP LEARNING. But it is substantially seen that both the terms are faultily used interchangeably. So let us find out what is the difference between the two and how both the terms are interrelated with each other.


Machine Learning vs Deep Learning
Pic Credit: Linked IN | Machine Learning vs Deep Learning

The term machine learning refers to a technology which enables a device to perform a task without any human intervention. In other words, machine learning is that field of data science which consists of the algorithms that perform the learning procedure without human assistance.

While deep learning is referred to the procedure which is used to implement machine learning. Deep learning is a configuration of machine learning which is inspirited by the framework of the human brain and is peculiarly efficient in feature detection.


  • Machine learning has various approaches and maximum of them hold ‘Artificial Neural Networks’ (ANNs).
  • An Artificial Neural Network (ANN) is a data processing prototype which is built on the basis of the structure and operation of biological neural networks.
  • One such distinct type of ANN which has grasped the maximum attention and seen a significant progress is ‘DEEP LEARNING’ network.
  • Thus it is concluded that deep learning is a procedure used to implement machine learning.
Pic Credit: Xenonstack | Machine Learning vs Deep Learning
  • In general, the nodes or neurons are positioned in layers: an input layer, an output layer, and one or more hidden layers.
  • A neural network usually has two to three nodes while there are hundreds of nodes in the deep learning network.
  • In deep learning method, each of the nodes is determined using some mathematical functions which are basically used to narrate that how the nodes operate in the presence of an input signal.
  • The signals at the input layer are applied from a digitized representation of something like an image, a sound, an audio etc.
  • The response of each node is combined which results in an output such as recognition of the objects in the image or words in the sound or audio which were applied at the output.
  • Thus the system learns, by developing a learning set of data. In general, there is a set of inputs whose desirable outputs are known.
  • The mathematical functions of the nodes tune-up on its own and thus the system gets finer at its performance.


Simple Neural Network and Deep Neural Network
Pic Credit: Xenonstack | Simple Neural Network and Deep Neural Network

It is observed that when the available amount of data is small then deep learning is not that effective while machine learning leads in this scenario. The reason behind this is that the algorithms of deep learning operate more effectively when the amount of data is large.


Machine learning algorithms depend on low-end machines while deep learning algorithms are dependent on high-end machines. The reason for such dependency is that deep learning algorithms include GPUs as an integral part of their working.


Machine learning algorithms and deep learning algorithms have different problem-solving approaches. When we solve a problem using machine learning algorithm, the problem is broken into different levels. At each level, the problem is solved and then the solution of each level is combined to form the solution of an entire problem while in deep learning the problem is solved end-to-end as a whole.


In general, the training time of deep learning algorithms is high due to the presence of so many parameters in the deep learning algorithms whereas machine learning comparatively takes lesser time in the training procedure.

This is then reversed for the testing time. The testing time of machine learning is higher than deep learning.


Machine learning algorithms interpret crisp rules while deep learning does not i.e. result interpretation is more appropriate in machine learning while deep learning lacks this ideality.


Machine learning and deep learning have their applications in various areas, some of the major applications are:

Medical Diagnosis

Machine learning and deep learning are used in medical imaging. The algorithms of machine learning are used in tumour detection and monitoring the development of a tumour, blood flow quantification and visualisation etc.

Online Advertisement and Digital Marketing

A very vast usage of machine learning and deep learning is seen in the online and digital advertisement. It allows the marketers to determine how to engage the consumers at maximum and earn the best profit.

Computer Vision

Computer vision is a science which deals with the automatic extraction of important and useful information from a number of images. Machine learning and deep learning have the major usage in this area only. Computer vision is used in various fields like robotics, transport, process control etc.

Use cases for Machine Learning
Pic Credit : Medium | Use cases for Machine Learning


The entire material thus concludes the basic concept of machine learning and deep learning, their technological aspects and their applications in the current scenario.

Hopefully, this blog could help you learn about the machine learning and deep learning algorithms and identify the differences and similarities between the two. Feel free to explore and gain more knowledge about artificial intelligence and computer vision for getting better insights of the AI world.

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