Understanding Artificial Intelligence vs Machine Learning vs Deep Learning

We know that Earth is surrounded by atmosphere, and it comprises layers of atmosphere. The layer which is suitable for human beings to survive is troposphere. There are three more layers which we would not usually discuss about. However, the key focus is that the atmosphere is the umbrella term for all the layers that reside in it. Similarly, Artificial Intelligence is the umbrella term, or we can say that it is the presentation layer under which Machine Learning and Deep Learning exists.

Watch this Artificial Intelligence vs Machine Learning vs Deep learning video:

AI vs ML vs DL

The below diagram gives more clarity on Artificial Intelligence vs Machine Learning vs Deep Learning:

To understand Artificial Intelligence vs Machine Learning vs Deep Learning, we will first look at Artificial Intelligence.

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Artificial Intelligence

According to John McCarthy, ‘The science and engineering of making intelligent machines, especially intelligent computer programs’ is Artificial Intelligence.

To understand Artificial Intelligence vs Machine Learning, let’s look at the categories of Artificial Intelligence.

There are two categories of Artificial Intelligence:

Weak Artificial Intelligence

In this, the machine programs act according to a well-defined response. They are confined to a set of rules that we provide, and they give response within the domain of those rules.

A common example can be a home appliance like an oven. We can set the timer and temperature in it according to the need. It performs the task according to the given instruction. It does not have the ability to make decisions and make any changes by itself. So, it comes under the category of Weak Artificial Intelligence.

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Strong Artificial Intelligence

Machine Learning and Deep Learning comes under the category of Strong Artificial Intelligence. It involves designing of algorithms for machines that try to learn by themselves using the input data and improve the accuracy in giving outputs.

Examples of Strong Artificial Intelligence are speech recognition, visual perception, and language translation. In language translation, a machine extracts the meaning of words and then the meaning of sentences. After that, it searches for similar-meaning words and sentences in another language and then translates them to that language. As it requires many tasks to accomplish translation and involves decision-making to put everything right, it comes under the category of Strong Artificial Intelligence.

By now, we have seen ‘What is Artificial Intelligence?’ After this, to make the picture more clear for Artificial Intelligence vs Machine Learning vs Deep Learning, we will now move on to Machine Learning.

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Machine Learning

Machine Learning is the subset of AI where Machine Learning algorithms are designed in such a way that the machine tries to learn by itself without being explicitly programmed on each and every instruction. So, as it will be exposed to more and more data, it tries to internally modify itself and adjust according to the data to which it is exposed so that it will not rely on human experts to program them.

There are basically three types of Machine Learning as shown below:

Supervised Learning

In supervised learning, the machine is provided with the labeled dataset. It already has input and output parameters. So, when the machine is given a new dataset, the supervised learning algorithm examines the data and produces the correct output according to the labeled data.

Use case: Detecting cancer patients

There would be some parameters and symptoms given for detecting cancer patients. The machine will try to classify the patients according to the symptoms to determine whether they are having cancer or not. So, supervised learning is best for classification and regression problems.

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Unsupervised Learning

In unsupervised learning, the machine would not have any labelled dataset. The algorithm is designed in a way that it tries to learn by itself without any supervision of data. This involves clustering of data.

Example: Consider few objects such as pencil, eraser, and matchbox

Here, the machine does not even know what these objects are; rather, it makes clusters of similar objects, and when any input dataset is given, it gives the output by examining the data it has clustered.

Netflix recommendation system works on the same technique as it saves the users’ watched history and recommends a similar content to the user.

Reinforcement Learning

In reinforcement learning, the algorithms are designed in such a way that the machine tries to find an optimal solution. It adopts the principle of reward and punishment, and by this approach it moves to the correct result.

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Consider a scenario where a young cricketer tries to learn the technique of hitting a 6 for a particular shot. Whenever he tries to play the shot and misses it, he gets a score of −1.

The bowler bowls again, and the batsman tries to hit that shot by adjusting his position. After trying for five to six times, he finally hits a 6 with a suitable position and gets a score of +6.

This way he learns the technique for hitting a 6. Reinforcement algorithm works in a similar way.

Watch this Supervised vs Unsupervised vs Reinforcement Learning Tutorial

AI vs ML vs DL

Types of Machine Learning at a Glance

Now, while understanding Artificial Intelligence vs Machine Learning vs Deep Learning, here is the last topic down the hierarchy that is Deep Learning.

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Deep Learning

Deep Learning has evolved from Machine Learning. It works in a layered architecture and uses the artificial neural network, a concept inspired by the biological neural network. The human brain usually analyzes and converts the information it receives and tries to identify it from the past information the brain has stored. The brain does this through labeling and assigning information into various groups in a fraction of a millisecond.

In a similar way, Deep Learning algorithms are trained to identify patterns and classify various types of information to give the desired output when it receives an input.

So, to understand Deep Learning vs Machine Learning, you must know the difference between Machine Learning and Deep Learning and the major difference is that we need to provide the features manually in Machine Learning. But in Deep Learning, it automatically extracts features for classification which in turn demands a huge amount of data for training DL algorithms. So, in Deep Learning, the accuracy of the output depends on the amount of data.

Now, let us look at artificial neural networks in Deep Learning.

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Artificial Neural Network (ANN)

It is a concept inspired by the biological neural network. It consists of three layers:

  • Input Layer: The input layer is used for taking the input data from external sources and then passing it on to the hidden layers of the neural network. It does not perform any computation.
  • Hidden Layer: This layer consists of many hidden layers. All the computation is performed in this layer. After all the computation is done, it passes the output to the output layer.
  • Output Layer: This layer is used for computing and giving the output to the outside world.


These layers consist of nodes that are interconnected with each other. The nodes interact with each other with the help of links by which they are connected. This connection of nodes is designed in such a way that it produces output for a given input.

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The links are associated with a real number that is called the weight of those links. These weights are initialized randomly, and hence there could be a large difference between the actual values and the predicted values. Due to this, it will not give the desired output in one iteration.

Even after the weight is assigned and the computation is done if it does not give the desired output, we go back and update the weight of the link with the current value to get closer to the desired output. We do this progressively until we get the best possible output.

Also, the weights assigned to the links decides how fast the triggering of the activation function will occur.

There is another term ‘bias,’ which is used to decide when to trigger the activation function.

Now, we will see the details of the activation function.

In the real world, the data is always 3-dimensional. For example, let’s say, an image of a car is given as an input to the neural network; we can plot the length and height of the car in a 2-D plane. However, there are a lot more attributes to be considered while computing to recognize it as a car. The computation of this data is a complex task. So, to reduce this difficulty, we use the activation function. It pumps the wide range of values assigned to the data into a specific domain so that computation becomes easy.

This is how the artificial neural network works and helps in achieving perfection in Artificial Intelligence.

To summarize, Artificial Intelligence is an umbrella term, and Machine Learning and Deep Learning are the subdomains of this field that help in achieving Artificial Intelligence.

Hopefully, this tutorial gave the hierarchical description of Artificial Intelligence, Machine Learning, and Deep Learning and cleared the confusion among these terms.

This is all about Artificial Intelligence vs Machine Learning vs Deep Learning.

Watch this Artificial Neural Network Tutorial Tutorial

AI vs ML vs DL

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