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Machine Learning (ML) and Myths of AI vs. ML

Machine Learning (ML) the future in evolution

Introduction to Machine Learning !!

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn the concepts using data and help you clear the myths around the rise of AI & Machine Learning !!

We know human learn from their prior experiences and machines learn from the instructions given by humans.

But think of situation where we humans can train the machines to learn from their historical data and to do what humans can do – that is  what Machine Learning is !!

How do we define Machine Learning (ML):

Machine learning (ML) is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

ML focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.

The primary objective is to allow the computers to learn automatically” without human intervention or assistance and adjust actions accordingly.

However, ML is lot more than just learning, it’s also about Understanding & Reasoning

Co-Relation between Artificial Intelligence & Machine Learning

Currently the terms – AI & ML is closely connected and it’s not wrong to mention that the abstraction level between these two words is a fairly thin line and that they will be inhume changeably used.

However once I say ML or Computer Science, what most of the individuals voice communication is that the usual Sci-Fi movies of Hollywood

You think that there’s progressing to be some TX 9000 machine that’s progressing to return up from the long run, goes to destroy entire humanity, you begin panicking and you may simply suppose that there’s not progressing to be a need of programmers within the future and plenty of theories like that?

So put down your killer flick theory aside for a second to let’s say AI and ML

Currently ML and AI of these square measure branches of engineering with they’re closely connected, however in step with Pine Tree State, what my personal thought is machine learning is closely associated with data mining instead of AI

Visit my link below to learn more about Artificial Intelligence !!

https://www.infocube.in/index.php/2020/02/03/what-is-artificial-intelligence-and-history-of-artificial-intelligence-and-categories-of-artificial-intelligence-and-machine-learning/

While AI is totally a special issue however what you think that of machine learning is closely associated with data mining and you’ve got been already exploitation it quite an ton.

Now, you may be asking hey where we tend to square measure the exploitation machine learning? currently though you’ve got simply detected the term ML, however you may already remember of the term called data mining/processing.

A Bit about Data Mining

Now data mining has been there since the evolution of information and computers that are into the planet quite an ton and every one the items that you just see. straightforward examples would be Spam emails

You see that a number of your emails square measure in your inbox and a few of them square measure into Spam box. what’s that? that’s machine learning! Rather closely that’s data mining.

There’s a large chunk of information and your program and algorithmic program is meant in such a fashion in order that it will predict that whether or not this email is spam Or is it a decent email that must be delivered in Inbox.

Typically sensible email conjointly finally end up within the spam and spam email finally ends up within the inbox. So is essentially a decent example of machine learning, at a really tiny level.

That was version one of machine learning, which we have already witnessed in our day to day life is machine learning version a pair of. So if I quote the machine learning at a really broad scale, there are unit a few of elements that you simply ought to be upset about

Let’s look at a sample application of Machine Learning !!

First of all, may be a vast information set. Information set which will predict tons of things, as an example – If I simply show you a chair you’ll be able to say hey, that’s a chair!

However if I say that that’s a wooden chair, that’s a glass chair and there are a unit plenty of gazillion, bazillion kind of chair; you’ll be able to see the distinction between of these chairs and may still predict that’s the chair.

But if I simply raise you to jot down a program for that, that would are nightmare for you. for instance If you’re simply writing a program that it ought to have a four legs and a few wood texture that will be a chair.

Challenges:

But what concerning once I say that -hey, it are often simply a centralized table, having a central base and a glass sitting space that’s conjointly a chair, however you can’t write a program for that and for such state of affairs we have a tendency to need an enormous range of knowledge sets, that’s the first issue.

The second issue that information (data) set is being pitched to one thing called classifier. That is one more a big term, however rather I would say that’s simply associate with an algorithm which can confirm 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 currently based on what kind of data you are supplying, your classifier can classify the image or any other thing. During 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 ab is that 99% chair? Is it 80% chair and is it 70 percent chair?

Therefore, 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 that hey in the future is going to be the AI (Artificial Intelligence) and the Machine Learning are going to learn to write the code, So there will be no need of software engineer or a programmer !!

Does AI & ML take away the jobs ?

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! however did it open additional range (plethora) of job as compared that the job that is taken? evidently, it’s done!

The same phenomenon 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!

Conclusion

So on a whole note, there is no such thing to be worried about that because the evolution and future is AI (Artificial Intelligence) and ML (Machine Learning), we don’t need programmers in future or all of support engineers job would be at risk. In fact we do need more programmers in future

All you have to be prepared is for the next learning curve in your career and be prepared for the upcoming fascinating challenges, but I would call it as opportunities !

Next what ? – hook onto my next article on types of Machine Learning and their applicability in todays date

till then visit this link for an overview http://So on a whole note, there is no such thing to be worried about that the future is AI (Artificial Intelligence) and Machine learning and we don t need Programmers in future. In fact we do need more programmers in future

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