What is machine learning and how to learn it
Hi Everyone, Todays topic is really interesting. I'm going to discuss about machine learning
What is machine learning in Layman's term and how you can get started in
machine learning. A lot more is about to come. So let's get started!
ML V/S AI and the Myth ?
Now the terms - Machine learning and AI (Artificial Intelligence) is closely related,
And it's not wrong to say that the abstraction level between these two words is fairly thin and they can be Interchangeably used, but when I say machine learning or artificial intelligence, what most of the people saying is the same old terminator movie. You think that there is going to be some machine that is going to come up from the future, is going to destroy entire humanity, you start panicking and And you will just think that there is not going to be any need of programmers in the future and a lot of theories like that?
Hey hold down there! This is not actually a fictional movie, if this could have been true
so we should stop testing about with a Gamma-Rays because it can generate a Hulk and we should stop looking into the space because we may find aliens and that may invade the earth. There might be a Thor coming up to save all of you and there might be a Spider man roaming around and who knows, there might be a Batman, too! So hold on your horses
we need to talk a lot about machine learning and what actually it is. So putting down your
terminator movie theory aside for a minute.
Let’s talk about machine learning and AI.
Now machine learning and AI all these are branches of computer science. They are closely related, but according to me, what my personal thought is machine learning is closely related to
data mining rather than AI.
AI is completely a different thing But what you think of machine learning is closely related to data mining And you have been already using it quite a lot. Now, you might be asking hey where We are already using machine learning? Now although you have just heard the term machine learning But you might already be aware of the term known as data mining. Now Data mining has been there since the evolution of data and computers which have been into the world quite a lot and all the things that you see.
Simple examples would be Spam emails. You see that some of your emails are in your inbox and some of them are into Spam box.
What is that? That is machine learning!
Rather closely that is Data mining. There is a huge chunk of data and your program and algorithm is designed in such a manner So that it can predict that whether this email is spam
Or is it a good email that needs to be delivered in Inbox. Is it always perfect?
No, not at all! Sometimes good email also land up in the spam and spam email ends up in the inbox. So that is basically a good example of machine learning, at a very small level.
But now things are changing. That was Version 1 of machine learning. Now what we are seeing in our day to day life is machine learning Version 2. So, how actually this is working all nowadays?
So if I talk about the machine learning at a very broad scale. There are a couple of components that you need to be worried about. First of all, is a huge dataset. Dataset that can predict a lot of things,
For example, If I just show you a chair you can say hey, that's a chair! But if I say that that's a wooden chair, that’s a glass chair And there are tons of gazillion, bazillion type of chair.
You can see the difference between all these chairs and can still predict that that's the chair. But if I just ask you to write a program for that, it could have been nightmare for you.
For example If you're just writing a program that it should have a four legs and some wooden texture That would be a chair. But what about when I say that -hey, it can be just a centralized table, having a central base and a glass sitting area, that is also a chair, but you cannot write a program for that and for such situation We require a huge number of Data sets. That's problem number one.
The second thing that data set is being pitched to something known as Classifier. Which is again a big term, big big term but rather, I would say that is just an algorithm which can Determine the output based on whatever the Data is being fetched. And as we all know the more data We are going to have the more prediction capability is going to be there.
So now based on what kind of data You are supplying, your classifier can classify The image or any other thing.
In this Example, We are just taking an image of chair so it can predict that image of chair with some certain amount of confidence that it can be chair. It can never be 100% sure but it's always about the ratio of How much confidence that it's showing that is that 99% chair? Is it 80% chair and is it 70 percent chair? So this is all on a broad scale what the machine learning is. What we are trying to teach with the machine and Yes, I know some of you are worried about, hey in the future It's going to be the AI and the machine learning are going to learn to write the code So there will be no need of programmer.
Hold down your horses! Who told you that first of all? With the evolution things changes quite a lot I do agree But this is almost similar to the strike that I saw in my childhood, when people were opposing the computers. Everybody in the government department Private sector was saying that hey if computers will come up, they will take our job. Did computers did that? Perhaps! But did it open more number of job as compared to that the job that is taken? For sure, it has done!
The same thing Is applied here. Is it going to take the job of programmers? Who knows ? But is it going to open up more responsibilities and more scenarios of working jobs? For sure it is going to be there! So on a whole note there is no such thing to be worried about that machine learning the future is machine learning and AI and we don't need Programmers in future. In fact we do need more programmers in future.
So now that you understand that how machine learning work on a simple scenario, a huge number of Data set being given to classifier and based on That data set, it just do some processing and tries to predict the results. That's basically your machine learning, being applied at a lot of places. Spamming is one of them.
Recently, if you saw the Google’s new product, you can just open up your camera app and can see the restaurant’s name and based on handwriting prediction image prediction and logo prediction it can query to the humongous amount of Data set that is present at the Google and can find out the ratings of the restaurants the name of the restaurants and some reviews about the restaurant. That's just one example of machine learning. Have you used some kind of app which predicts- How you will look like in your 80's or your 90's? How your face is going to get at some deformation? Your skin is going to get some kind of deformation. This is all based on machine learning.
Small level of examples, but yes, this is all based on machine learning! So now that on a very big scale you understand, what is machine learning
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