As you have been progressing towards building AI readiness, you must have come across a very common dilemma between Artificial Intelligence (AI) and Machine Learning (ML).

Many times, these terms are used interchangeably but are they the same? Is there no difference in Machine Learning and Artificial Intelligence? Is Deep Learning (DL) Also Artificial Intelligence? What exactly is Deep Learning? Let us see

This Flow chart will help us get the overview of Artificial Intelligence


To read about Artificial Intelligence Go to What is AI & What Not ? and Human vs Artificial Intelligence

Now you should be sure about what AI is, So we can jump to what is Machine learning below.

What is Machine learning

It is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data).

The intention of Machine Learning is to enable machines to learn by themselves using the provided data and make accurate algorithms for Predictions/ Decisions.

Now lets understand Machine Learning in a simple way. In literal Machine Learning is "Machine" + "Learning".

It means that Associating Machines with Learning. Lets go dig it out :)

How Machine Learning works ?

Machines Learn in a similar manner in which humans learn. Well Lets quickly see How Humans Learn ?

How Humans Learn ?

To understand the human learning process, let us illustrate it through a simple sequence of activities involved.


Machine Learning Process

A computer learns in a similar way. However, it needs a lot of information, to ensure that it recognizes things accurately. Machines need to be trained to make decisions and to act based on goals

How to Train Machines ? Computers need a lot of information to ensure accurate recognition of things

Lets Take an Example , Now think, how can a computer recognize a flame?

This could be using color, size, shape, origin, temperature, etc. Once it recognizes the flame, it needs to be trained to make the decision to act, as in this case, whether it should touch the flame or not?
This is based on the goals set for the computer.

For example, a computer set the goal to extinguish the flame by touching or stamping. So, it will see a flame and act to touch it and extinguish it, whereas another way to avoid burning and damage is to see a flame as danger and not to touch it.

Now think, how will a computer differentiate between an actual flame and a photo of the flame?

A wrongly trained computer may try and extinguish the flame in the painting or touch a live flame again to get damaged. So, is it possible that they can discern based on temperature, movement, etc?

Capability of Machines

To make the machine learn, first we need to see the capability of the machine.

Capabilities of a Machine:-

  1. Cameras for capturing images.
  2. Receivers for capturing sound.
  3. Convertors and Speakers to produce sound.
  4. We Can provide them with sensing devices that can act like skin, eyes, ears, mouth, and noses . Some of these are called sensors.
  5. Sensors are man-made (artificial) devices, usually made of specific materials and connected using electronic signals. They sense chemicals, waves, light, and other things and interpret them

Artificial Intelligence vs Machine learning

But even though both AI and ML are based on statistics and mathematics, they are not the same thing.

Artificial Intelligence Machine learning
It is the study of how to train the computers so that computers can do things which at present human can do better. Machine Learning is the learning in which machine can learn by its own without being explicitly programmed.
Perform tasks that require human intelligence. Helps machines to learn certain things on their own.
The aim is to increase chance of success and not accuracy. The aim is to increase accuracy, but it does not care about success.
It work as a computer program that does smart work. it is a simple concept machine takes data and learn from data.
AI is decision making ML allows system to learn new things from data.
AI leads to intelligence or wisdom. ML leads to knowledge.

AI try to make machines intelligent to take accurate desicions, whereas Ml try to make machine learn how to do certain things.

What is Deep Learning

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.
Deep learning is an important element of data science, which includes statistics and predictive modeling.

Deep Learning have very complex algothims and require very large amount of dataset.

Deep learning utilizes both structured and unstructured data for training.
Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.

Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.

How it Works :

Deep learning is based on ML but it can work with unstructured data and learns on its own through reading the data, so it requires large amount of datasets called Big Data which is drawn from sources like social media, internet search engines, e-commerce platforms,etc.

These datasets are then processed through complex ML algorithms.

Machine Learning vs Deep learning

Deep learning is a type of machine learning, which is a subset of artificial intelligence.

Machine Learning Deep Learning
Machine Learning is a superset of Deep Learning Deep Learning is a subset of Machine Learning
The data represented in Machine Learning is quite different as compared to Deep Learning as it uses structured data. The data representation is used in Deep Learning is quite different as it uses neural networks(ANN).
Machine learning consists of thousands of data points. Big Data: Millions of data points.
Machine Learning is highly used to stay in the competition and learn new things. Deep Learning solves complex machine learning issues.

Your data is used by companies to do deep learning and make products on current trend in mentality and what people likes.