Clear The Confusion-Artificial Intelligence vs Machine Learning vs Deep Learning


Raise your hand, if you are stuck in confusion between Artificial Intelligence, Machine Learning and Deep Learning. Are you one of these? If yes, then you have clicked the right page. Here, you will get your answers.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the buzzing technologies of this digital age. These three terms not only have different spellings but also refer to other things. 

However, we use these terms interchangeably; the truth is they are different but a part of the same branch, i.e. computer science. Take a look at the image and understand the difference.

As you can see in the image,  there are three concentric circles; Deep Learning is a subset of Machine Learning, which is also a subset of Artificial Intelligence.

See how the technologies of the same departments are competing with each other and serving us differently.  

Google Trends

Let’s dig in to understand the difference between AI, ML and DL:

Artificial Intelligence 

Machine Learning

Deep Learning

AI or Artificial Intelligence is the study/process that allows machines to mimic human behaviour through a particular algorithm.

ML or Machine Learning is the study that uses statistical methods allowing machines to improve and learn with experience.

DL or Deep Learning, is the study that uses Neural Networks (similar to neurons present in the human brain) to imitate functionality as like a human brain.

AI is the broader family that consists of ML and DL both as it’s components.

ML is a subset of AI.

DL is a subset of ML.

AI is a computer algorithm that shows intelligence by decision making.

ML is an AI algorithm that allows the system to learn from data.

DL is an ML algorithm that uses deep(more than one layer) neural networks to analyse data and deliver output accordingly.

Its motive is to increase success chances not accuracy.

Its motive is accuracy not the success ratio.

It ranks high in providing accuracy when it trained with large data amounts.

Types -Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI).

Types-Supervised Learning, Unsupervised Learning and Reinforcement Learning.

It is considered as neural networks with different parameters layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks.

Examples- Google’s AI-Powered Predictions,Commercial  Flights Use an AI Autopilot, Ridesharing Apps Like Uber and Lyft etc.

Examples- Virtual Personal Assistants: Siri, Alexa, Google, etc. Email Spam and Malware Filtering.

Example- sentiment based news aggregation, Image analysis and caption generation, etc.

Artificial Intelligence

As it’s clear from the name, artificial intelligence can easily be interpreted as human intelligence incorporated into machines. But it’s a broader concept that contains everything from  Good Old-Fashioned AI (GOFAI) to futuristic technologies like deep learning. When a machine completes tasks it stipulates rules that solve our problems (algorithms), and such intelligent behavior is called artificial intelligence.

We can say that AI is the broadest way that one can think about advanced intelligence in the computer world. Anything that machines can count in artificial intelligence. 

AI-powered machines are classified into two groups — general and narrow. The general AI machines solve problems intelligently. Example- Machines can move and manipulate objects, recognize whether someone has doubts, or solve other problems. You can also seek help from AI developers in India to help you understand the difference between the two.

And, the narrow AI machines can do specific tasks very well, and even sometimes better than humans — though they have a limited scope. Example-The technology used in Pinterest to classify images.


  • Day to Day Application
  • Provides Digital Assistance
  • Handle Repetitive Jobs
  • Medical Applications
  • Reduction of Error

Machine Learning

As I have mentioned above, machine learning is a part of AI that gives systems the ability to learn automatically and improve from the experience without getting explicitly programmed. ML concentrates on the development of computer programs that can access and use data to learn for themselves.

The learning process begins with observations or data, for example, direct experience or instruction, to look for patterns in data and make better future decisions based on our examples. The sole motive of ML is to allow computers to learn automatically with no human intervention or assistance and accurate predictions.

Machine learning training involves providing a lot of data to the algorithm and enabling it to learn more about the processed information.


  • Facilitates accurate medical predictions & diagnoses
  • Simplifies Product Marketing and assists in making accurate sales forecasts.
  • Improves the precision of financial rules and models.
  • Recommend the right product
  • Increases the predictive maintenance efficiency in the manufacturing industry.

Deep Learning

As I have mentioned above, Deep Learning is a subfield of machine learning and concerned with algorithms that are inspired by the brain’s structure and its functioning known as artificial neural networks. In short, DL is the next evolution of ML.

Just like humans, it uses its learning and past experiences to identify patterns and classify different types of information; deep learning algorithms can be taught to perform the same tasks for machines.

The brain tries typically to decipher the information it receives. It gets the information through labelling and assigning the items into different categories. When we receive new information, our brain compares it first to the known thing before making a sense,, and that's the same concept of deep learning.

Example- artificial neural networks (ANNs) are algorithms that aim to copy our brain's power in making decisions. 

Understand the subtle difference between deep learning and machine learning:

Example, DL can automatically find the features used for classification, whereas ML needs these features to be provided manually.

Furthermore, in contrast to machine learning, deep learning requires high-end machines and considerably large amounts of training data to give accurate results.


  • Maximum utilization of unstructured data
  • Eliminates the need for feature engineering
  • Avoids unnecessary costs
  • Elimination the data labeling need 
  • Deliver high-quality results

Wrapping Up

I am pretty sure that now you can understand the significant difference between these three technologies. Above I have explained all the major points that distinguish AI, ML, and DL with each other. Hope you get all your answers and now want to leverage these technologies then hire developers in India as they can help you better to decide what technology goes best for your business.

Let me also know your feedback and opinions in the comment sections on my blog. For more updates, keep following this space and stay updated.

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