AI vs Machine Learning vs Deep Learning vs Data Science

One thing that troubles almost every single person coming into the vast field of data science is the distinction between these buzzwords that look similar but in reality, works in different domains. These words are Artificial Intelligence, Machine Learning, and Deep Learning. How are they different from each other ie. AI vs Machine Learning etc?

How are they related to the much talked about and vast field of Data Science? This article tries to set clear lines between these terms and exemplify the functioning and usage in the industry.

What is Data Science?

In the present scenario of ultimate connectivity of everything around the globe has resulted in the generation of huge amounts of data. The big question now arises: What do we do with this available data? Is this data ready to be processed? This is where data science steps in. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines.

Data science is basically an extension of statistics dealing with a large amount of data with the help of computer algorithms. Data science converts the available raw data into a useful computer-compatible form.

This means the data can be molded according to specific requirements. It includes data cleansing, data preparation, and data analysis. This processed data can now be used as input for various machine learning algorithms. Data science usually deals with technologies like R, Python, SQL, Hadoop etc.

AI vs. ML vs. DL

We have seen that data science is the upper domain that provides processed data. The way this data is processed to get the output deals with technologies like AI, ML, and DL. There are many ways to distinguish between these terms. However, there are certain differences that hold true in many cases.

First of all, they hold a chronological order. The concept of Artificial Intelligence was introduced first, and then machine learning was developed. After that deep learning gained momentum. Deep Learning is a subset of Machine Learning. Machine Learning is a subset of Artificial Intelligence. This development looks like a top-down approach where with the advent of new technologies, advancements of deep learning may be developed in the future.

Being said that, it means that all ML is AI but all AI is not ML. Similarly, all DL is ML but not all ML is DL.  This can be understood with the given Venn diagram.  


Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. (Source: Wikipedia)

It means it is a way to enable the computer to think and make decisions just like human beings. It lets computer function on its own without human interference. The computer can make its own decisions in an open human interactive environment without any human support. AI can be just a few lines of if-else statements or even pages of lines of code. It all depends on the context they need to be used in.


Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” with data, without being explicitly programmed. (Source: Wikipedia)

It is a subset of Artificial Intelligence. It usually deals with self-learning of computers using the dataset and improvising the output from experience without being explicitly told what to do. This can then be used to predict future patterns or trends. Machine learning could be composed of statistical analysis (Unsupervised learning) or predictive analysis (Supervised learning). Recommendations on Netflix, Instagram, and Facebook make use of machine learning algorithms by analyzing past activities of the user.


Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. (Source: Wikipedia)

It is a more specific class of algorithms used in machine learning. It uses a brain like structure for functioning, called an artificial neural network. It deals mostly with predictive analysis. Deep learning algorithms use a hierarchal form of learning. Each algorithm at the lowest level deals with low complexity. It uses what it learned to create a statistical model output. This process continues through several levels/layers of the neural network. Iterations continue until a decent acceptable accuracy is reached.

AI vs. ML

Let’s take an example to clarify the differences between these two. Suppose there is a taxi company that sends a taxi to you whenever you book one, similar to Uber. It is synced with the map of the city that it operates in. Whenever you enter the pickup and drop location, the application checks the data of all previous rides along the same route. It then predicts an estimated cost for the ride. This is machine learning.

The same application also tells you the route that you will be taken through. If there is traffic congestion anywhere in the route, an alert is issued in form of voice stating “Traffic Jam Ahead!”. Here the application finds an unusual situation and takes a decision on its own that it is unfavorable and notifies the user. This is artificial intelligence.

ML vs. DL

Suppose the same application, that we have considered before has a feature that it identifies the car plate number and matches it with the allotted taxi’s number. If it is the same, the user is notified. This way the user doesn’t have to look around for the taxi. Here a neural network is employed that captures the taxi’s number plate and read the numbers from the image and matches with the dataset. This is deep learning.

We can say that all the terms that we have discussed so far are interrelated with each other. However, conceptually they hold major differences. As a person passionate about data science, these differences should be well understood. These lines of difference have now been drawn.

Reference Article on Introduction to above techniques Read More

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