You know if you are interested in this field and if you come from the technical background then you know this is probably the best time to get into it right any six months till a one-year delay you just want to lose of it what we can do some ground right because this is the place where the demand for talent is far more than the supply for talent there are of course others challenges such as you know data cost and computing power but these are all things that actually scale up very very rapidly right.

You know computing challenges or data cost or data acquisition could actually be solved or may get solved in as lessons they give to twelve months time frame but it is the talent gap that holds companies back the most and from our own, you no understanding of the industry in India and the way our alumni have performed what we have seen is that you know machine learning jobs right now are easily paying about fifty percent higher in terms of strategies and your traditional IT failures right.

So the same you know if you compare apples to apples the same professional was probably earlier working in IT services role if he or she transitions to machine learning you know from our own animated asset we see similar kind of satellites as for the job demand today you know there is a shortfall of about a hundred thousand machines learning education scientist in India by the end of 2018 and there's no way that you know this demand is going to get met right but what is more important is that if you can present yourself as a worthy candidate right than there is a lot of option via so it is all about being trained beings in the machine learning and being able to showcase that expertise showcase that competence.

If you can do that then you know you will never be restricted by the number of jobs up there if you look in our own network of companies that regularly contribute to our programs and either alumnus and hires from the programs we believe there are about 600 firms you know that are actually looking for machine learning professionals in India.

You know those 600 actually split across you know there are many startups there is established tech companies there, of course, global Duffy names - Sub by Facebook Amazon Microsoft and there are also a lot of you know financial companies and even some from other sectors that are actually looking for machine learning engineers and designers and architects who can come and implement in the scale of solutions you're using machine learning algorithms and even in you know if you look at the industry or even if you look at our own pool when they go into the industry what we see is that it's split as I was telling you earlier it's split across media sectors right so first you know the tech companies are hiring for machine learning engineers and machine learning architects empty percent is in a very big way but it is not as if that opportunities restrict to just effect you know there are consulting companies there are finance companies you know there are dry.

I'll fix companies there are several startups as I said earlier right which are all looking for talented machines are often you know I get this question in terms of what kind of jobs can one expect what are the roles they're out there and you know what are the rules that one would be eligible for after doing a program like this or after learning machine gun so one of the most common rules that exist are around or is around data scientist right so data scientist is essentially a combination of data science and machine learning techniques and implementation of these techniques.

For a lot of companies data science rules primarily mean towards our increasingly I pointed towards machine learning so this kind of rules open up if your see is your senior is you have good amount of experience then you know leadership rules and Technology a chief data officer even architect rules a lot of our alumni actually transition into architect process we've seen and if you're just starting out in the industry then you know if you are let's say less than five years of experience then rules such as data engineers data endless machine learning engineers like these are all these are all rules that are being hired today as we speak and that increasingly need you to have knowledge of machinery let me also you know try and simplify machine learning I'm sure that many of you have effect all of you would have heard of machine learning is a term but what is what does this really mean see at the crux of it the crux of machine learning is basically the ability to train an algorithm to perform certain actions right there by deriving certain just like humans would have right.

That is what machine learning edges crack it's at its core it's broad the two broad buckets that it kind of breaks down into a supervised learning and unsupervised versions and very easy way to kind of you know understand supervised and unsupervised is that supervised learning is essentially where you know you you train an algorithm with a training data set right so you are giving inputs but - at the same time you also need you also know what the output should be so over a large data set which is known as the training data set you're actually creating their algorithm till its accuracy improves right and once the accuracy improves and goes beyond a certain threshold from there on the algorithm starts you know functioning by its own.

That is a very very 10,000 feet high level of supervised learning so if you do understand an example let's say you know if you were trying to train a little got some around identifying colors right and you know you have cards of various colors right so on one side you have input and you know what the output should be right because you are aware which color exists on each card right so initially what we you will do is you will train the algorithm which means you will shoot the color and then you will point to it okay this was a card with Lukas and similarly this was a card with Pinker and so on the right so you go through a large training data set till it reaches a point where you know you have to just show the card.

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