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Machine Learning vs Deep Learning: How are They Different?

  • Riya Kumari
  • Nov 08, 2020
  • Updated on: Feb 12, 2021
Machine Learning vs Deep Learning: How are They Different? title banner

Artificial intelligence, at the end of the first half of the 20th century, the technology had evolved greatly till the current time and has the great potential to address complex problems. 


Machine Learning and Deep Learning are the two terms that are among the hottest topics in the field of technology where machine learning is the part of Artificial Intelligence or AI, deep learning is a subdivision of machine learning. 


For a brief note, machine learning takes the chaos of data to teach a computer how to learn, deep learning uses many layers of processes to spot patterns, imitating the human brain working system.


Machine learning is an Artificial Intelligence technique, and deep learning is a machine learning technique.

The image  is showing the interrelation between AI, ML, and deep learning such that ML is subset of AI and deep learning is subset of ML

Interrelation between AI, ML and deep learning

So, are you still confused with these two terms? Here, we have collected all the details related to machine learning and deep learning that will clear all your doubts. However, these two concepts always coincide. 



Table of Content


  1. What is Machine Learning
  2. What is Deep Learning
  3. Machine Learning vs Deep Learning
  4. Future of Machine Learning and Deep Learning


What is Machine Learning?


Machine Learning, an application of AI, helps in giving computer systems the capacity to automatically memorize and enhance from experience without being explicitly programmed. 


In simple words, with machine learning, computer systems are programmed to learn from data, even without being explicitly programmed, they progressively improve their performance on a task.


Machine learning includes complex math and computer programming that combinedly perform a function with the dataset given to it and get better steadily over time.  


It is being used in a broad range of areas like art, science, and finance. We have numerous algorithms that are used for machine learning such as Decision Tree, Random Forests, Find- S, and Artificial Neural Networks.


There are three kinds of learning algorithms in machine learning;


  1. Supervised Machine Learning AlgorithmsUnder this type of learning algorithm, labelled data is fed algorithms, variables are explained to algorithms for acquiring and establishing interconnections. Also, in this learning, both input and output are defined.
  2. Unsupervised Machine Learning AlgorithmsUnder this type of learning algorithm, algorithms are trained over unlabelled data where they examine the data for pulling out correlations. For example, clustering algorithms use EDA to get patterns or to make groups.
  3. Reinforcement Machine Learning AlgorithmsTo teach a computer algorithm a multistep process for which they have clearly defined rules, reinforcement learning is used. Also, algorithms are made in such a way that they get a positive or negative signal to act for performing and completing a task.   


“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives” -Amy Stapleton


What is Deep Learning?


A deep learning model is designed to analyze data with a logical structure, similar to a human brain structure, for drawing inferences. In order to accomplish this, deep learning deploys a layered structure of algorithms known as artificial neural networks. 


Essentially, the structure of a neural network is inspired by the biological neural network of the human brain, that makes a more potent learning process than standard ML models. For example, 


  1. Whenever you watch Netflix, you have presumably observed its suggestions for what to watch.
  2. Some real-time music services pick melodies, dependent on what you have tuned in previously or tunes you have offered the go-ahead to or hit the "like" button for.
  3. Google's voice recognition and picture recognition estimations use deep learning.


Deep learning permits machines to take into account the complex issues even when utilizing an informational collection that is extremely different, unstructured, and associates. The more deep learning calculations learn, the more adequately they accomplish.


Differences Between Machine Learning and Deep Learning


Now, let’s drive towards Machine learning versus Deep learning. 


The primary contrast is thus the sort of algorithms used for each situation, although deep learning is more like human learning as it works with neurons. As compared to machine learning, deep learning takes more time to train. 


Machine learning needs the limited tuning capability for hyperparameter tuning whereas deep learning can be tuned in numerous ways.


In machine learning, the outlet is in a numerical pattern for division and scoring applications, whereas, in deep learning, the outlet can be in any pattern comprising free form elements like free sound and text. 


The below table summarizes the fundamental differences between machine learning and deep learning.


Machine Learning

Deep Learning


Uses algorithms to define data, learn from data, and make informed decisions based on its learning. 

Uses structured algorithms in layer to create artificial neural networks (ANN) that learn and make informative decisions by itself.


The data representation is different as ML uses structured data.

As the data representation, deep learning uses neural networks. 


Machine Learning is the subset of AI, the evolution of AI.

Deep learning is the evolution of Machine Learning that tells how deep is ML.


Machine Learning involves thousands of data points.

Deep learning takes millions of data points, ie. big data.


Outputs: It has a numerical value as output, for example, classification score

It has anything as output like a numerical value, free-from constituents (free text or sound)


Uses different automated algorithms that act as model functions and anticipates future events from data.

Deploy neural networks that transmit data information by processing layers to interpret data features and correlations.


Data experts observe algorithms to examine particular variables in datasets.

Deep learning algorithms are self-regulated and govern data itself to analyze specific correlations.

Besides that, here we will discuss in detail five major differences between machine learning and deep learning.


  1. Time


Due to the extensive datasets as required by deep learning models and involvement of ample parameters and complex mathematical formulas, deep learning algorithms consumes much time, from hours to weeks.


Opposite to that, machine learning models can take limited time, from minor seconds to some hours only.



  1. Human Intervention


Though with the machine learning system, a human needs to recognize and hand-code the applied features on the basis of types of data whereas a deep learning system attempts to acquire those features even without involving human intervention additionally.


For example, in facial recognition application, the computer program learns to recognize edges and facial lines and then important parts of faces followed by an entire depiction of the face.


Now, a lot of data is required to do this and as the program trains itself, the probability of getting correct answer increases. The training takes place with the implementation of neural networks without involving a human to recode the program. 


  1. Approach


In general, ML algorithms parse data into parts, and then these parts are consolidated to yield outcomes whereas deep learning systems take into account the complete problem closely.


Taking an example of distinct specific items in a picture, you would need to consider two stages in ML analysis, first is object location and afterwards object recognition.


On the other side, with the deep learning program, input data as pictures are fed, and after training, the program would give output as both the recognized items and respected location in the picture in a single outcome.



  1. Applications


Some machine learning applications are commute estimation, smart emails, banking and personal finance, evaluation and assessment. 


Also Read | Top Machine Learning Tools


On the other side, self-driving cars, virtual assistants, virtual recognition, fraud detection, etc are among the core applications of deep learning. 



  1. Hardware


Because of the quantity of data being transformed and the intricacy of the mathematical computations used in the algorithm, deep learning systems need significantly more potent hardware than modest machine learning systems.


Graphical preparing units or GPUs is the one type of hardware used for deep learning. Machine learning projects could execute at lower-end machines without as much computing power.


Machine Learning and Deep Learning are being applied in;


  • Information Retrieval- We use machine learning and deep learning for applications such as search engines, both text and image search.

  • Computer Vision- If we talk about this point then we use this for various applications such as facial recognition and number plate identification, and many more. Computer vision is a very employed technology in the field of ML.

  • Marketing- This is for applications such as target identification, and automated email marketing, especially in digital marketing.

  • Medical Diagnosis- In the medical area, it has a use in huge numbers and it is for applications such as cancer identification.

  • NLP- We use NLP or Natural Language Processing for applications like photo tagging and sentiment analysis.


You can explore machine learning applications and deep learning applications from the link.



Future of Machine Learning and Deep Learning


The demand for machine learning and deep learning is continuously increasing. They are in need especially for corporations who expect to endure to combine machine learning in their industry. The opportunities for machine learning and deep learning in the coming days are almost endless. 


The expanded use of robots is guaranteed, in assembling as well as in manners that can improve our regular daily existences in both major and minor ways. The medical care industry additionally will probably change, as deep learning assists specialists with doing things like to foresee or recognize cancer prior, which can save lives. 


Also Read | Introduction to Deep Learning and Neural Network


On the budgetary front, machine learning and deep learning are ready to help organizations and even people save money, contribute all the more carefully, and allot assets all the more proficiently. Also, these three regions are just the start of future patterns for machine learning and deep learning. 



"Deep learning is already working in Google search and image search; it allows you to image-search a term like 'hug.' It's used to getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe" -Geoffrey Hinton


Numerous zones that will be improved are still just a sparkle in designers' minds at present. So, in the future machine learning and deep learning are going to surprise us, and researchers are continuously exploring machine learning and deep learning.





Ideally, this blog has given you all the essentials concerning machine learning versus deep learning. Along with that we also read in detail about machine learning, deep learning, its future expectations, and a brief look at machine learning and deep learning major differences. 

So, we can say that machine learning uses algorithms to parse information, gain from that information, and settle on educated choices dependent on what it has realized. Deep learning structures calculations in layers to make an "artificial neural network" that can learn and settle wondered choices all alone.


Also Read | Machine Learning vs Data Science


In the coming future, we will surely discern many more exciting things related to machine learning and deep learning.

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