Today I came up with an interesting topic which you all are familiar with. That is none other than “Artificial Intelligence, Machine learning and Deep Learning".

I know that most of us have a great confusion will all these terms. Almost 60% of us assumed that all 3 terms are same in all the aspects. But thats completely wrong.
So, first of all, before we go further, let me resolve this confusion. AI which is known as Artificial Intelligence is considered to be an umbrella of Machine Learning. At same time Machine Learning acts as an umbrella for Deep Learning. The following image represents the complete difference of all these terms.

We all think that Artificial Intelligence was introduced recently. But, its also a wrong assumption. Why?

Yes, AI is not a new word to our world as AI was actually introduced in 1956. Even though it was coined during that era, it was not popular as it is today. The reason for that is the shortage of data. With few data, it is impossible to get accurate results. In this modern era, there is a tremendous usage of data. From Data Statistics, it represents that at year 2018, data usage was nearly 4.4 Zettabytes, but at year 2020, it climbs upto 44 zettabytes. Along with enormous data, now we deal with advanced algorithm and high-end computing power & storage that can handle with these enormous amounts of data as a result it is expected nearly 80% of enterprise will implement AI within next 12 months.

So, it is much better for us to have a good knowledge on Artificial Intelligence, Deep learning and Machine learning.

First up, Artificial Intelligence (AI)! What's going on here?

AI is just any code, strategy or calculation that empowers machines to mirror, create and show human perception or conduct. We are in, what many allude to as, the period of "powerless AI". The innovation is still in its earliest stages and is required to make machines fit for busy and all that people do, in the time of "solid AI". To change from powerless AI to solid AI, machines need to become familiar with the methods of people. The procedures and cycles, which help machines in this undertaking are extensively ordered under AI. Machines learn in dominatingly two different ways. Their learning is either directed or unaided.

In managed learning, machines figure out how to anticipate results with the assistance from information researchers. In unaided learning, machines figure out how to foresee results in a hurry by perceiving designs in input information. At the point when machines can draw important deductions from huge volumes of informational indexes, they show the capacity to adapt profoundly. Profound learning requires counterfeit neural organizations (ANNs), which resemble the natural neural organizations in people. These organizations contain hubs in various layers that are associated and speak with one another to figure out voluminous info information. Profound learning is a subset of AI, which thus, is a subset of man-made brainpower. The three innovations help researchers and examiners decipher huge loads of information and are henceforth pivotal for the field of data science.
I hope from the above information you all got a clear understanding about these 3 different terms!