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Artificial Intelligence vs. Machine Learning vs. Deep Learning vs. Data Science

Today in the article I'll be discussing the most fundamental thing in data science like what is the difference between artificial intelligence, machine learning, deep learning and data science.

because many of you still have various confusion regarding it so in this particular article I'm going to clear confusion and I'm going to tell you what exactly is artificial intelligence? , what exactly is machine learning? , what exactly is deep learning? and how do we use data science? considering all this particular technology and work.

But then again, I’m no expert here. This is the knowledge I’ve gained in the last few months of my data science and machine learning journey. I’m sure most of you will have better and easier ways of explaining things than I do, so I’ll be looking forward to reading your comments down below. Let’s get started then.

1] Artificial Intelligence (AI):

Artificial intelligence is the ability that can be imparted to computers which enables these machines to understand data, learn from the data, and make decisions based on patterns hidden in the data, or inferences that could otherwise be very difficult (to almost impossible) for humans to make manually. AI also enables machines to adjust their “knowledge” based on new inputs that were not part of the data used for training these machines.It enables the machine to think that basically means without any human intervention the machine will be able to take its own decision

Examples of AI application: Smart assistants, Self-driving car, Social media monitoring, Inter-team chat tool.

 So what AI application actually does? AI application app which actually uses machine learning and deep learning within them is basically an AI application it does some kind of task.

2] Machine learning (ML):

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Machine learning is a subset of AI. So what does machine learning help us to do? So machine learning provides us some statistical tools to explore and understand that particular data.

But how does a machine learn? You might ask. There are different ways of making machine learning. Different methods of machine learning are supervised learning, non-supervised learning, semi-supervised learning, and reinforced machine learning.

In case of supervised learning, we'll be having ‘some label data, some passed data’ and with the help of this kind of data, we'll be actually able to do the prediction for the future. So this kind of learning initially whenever we are making our model at that time we'll have this data in our hand previously only. And then we'll create a model train on that data and with the help of that kind of data we'll be actually creating a supervised machine learning model. In case of supervised we have passed labelled data, “we know what will be the output of this particular data”.

When we talk about unsupervised machine learning here we'll ‘not be having any labelled data’ that basically means in my data set we will not know “what is the output” so in unsupervised machine learning we usually solve Clustering kind of problems. Now here is the question arise what is clustering? So based on the similarity of that data it will try to group that data together and there is some mathematical concepts actually used inside. Most probably here are three different algorithms one is k-means clustering, Hierarchical clustering and Density-based clustering. These are the three popular clustering algorithms that we basically used in unsupervised machine learning

Now in case of reinforcement learning, “some part of your data will be labelled” and later on “some part of the data will not be labelled” so the computer or the machine learning model learn slowly by seeing the past data and it will be learning as soon as the new environment new data will be coming up.

So in ML, the important part is that we need to have data, it provides some statistical tools to explore and analyse the data.

3] Deep Learning (DL):

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. Deep learning is able to learn without human supervision, drawing from data that is both unstructured and unlabelled. So over here in deep learning, you create architecture which is called as multi neural network architecture. In deep learning also you have various techniques one is ANN that is Artificial Neural Network, the second one is CNN that is Convolution Neural Network and the third one is RNN which is called as a Recurrent Neural Network.

We actually using this concept of machine learning and deep learning and the main goal is to derive an AI application using this particular technique.

4] Data Science (DS):

Now data science is a technique which tries to apply all this particular part means all these techniques that is basically machine learning, deep learning and apart from that it also uses some mathematical tools like statistics, probability, linear algebra, etc.So a data scientist will have to work on ML, DL based on the type of use case by using some mathematical tools like statistics, probability, linear algebra and many more.DS may work in all three kinds of machine learning, DS may work in all three kinds of deep learning techniques

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Mainly data science is used to make business decisions. Data science is being used extensively in such scenarios. Companies are using data science to build recommendation engines, and predicting user behaviour, and much more. All of this is only possible when you have enough amount of data so that various algorithms could be applied to that data to give you more accurate results.

This is the basic difference between artificial intelligence, machine learning, deep learning and data science, we have a whole lot of these things that we basically learn when we want to become a data scientist, isn't it amazing?

So yes this was all about this particular article I hope you understood that we discussed that “what is the basic difference between artificial intelligence, machine learning, deep learning and data science.

So please share with all your friends who ever require this kind of help, who is interested in this topic or who want to become a Data Scientist.

And remember

Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.

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author

Shreeram Geedh

By Blogger

I am studying computer engineering. I came here because I love utilizing my knowledge and skill, utilize given opportunity effectively for professional growth, and to contribute in the best possible way for the betterment of the people and self.