You don't need to tell the algorithm you know which colors it starts predicting the cut all right so so that is what you essentially call a classification problem similarly regulation is another category of supervised done the other broad bucket of machine learning is unsupervised so this is where you are not training the algorithm with an e output data but what you are doing is you are essentially giving input data and the algorithm is supposed to interpret by grouping this data it's very common like you know a very common technique here is clustering that is used a lot for example.

Let's say if you give you know the population data or you feed population data of a city to an algorithm right it should by itself start creating clusters depending upon you know how many or all the males are clustered is one clear about the females that cluster is one group all the infants are clustered is one grow all the senior citizens across to this one group like that right so so that is some the algorithm has escapable of Dune that's how it's been that's the machine technique that you used it the clustering technique that you use it has a clustering logic that you use that right.

That is a simple example of unsupervised learning so so broadly machine learning breaks down into these two it there is supervised learning and then there is unsupervised learning and within those, there are of course a lot of techniques that one needs to know and more important techniques not just from a theoretical standpoint but techniques from an industry application Stanford like how do you apply this to industry and solve problems alright.

If that you know has gotten you curious right so then you need to know that what kind of skill sets do you need to have to be good in machine learning or to build a career first and foremost is that you should be good in points doesn't mean you have to be a mathematician or a math Olympiad Ian or all you have to be a statistician that's not the point is that you know basic algebra and you know 10 to 12 math is something that you will have to get back to and that is where the foundation of machine learning is based especially in linear algebra and so on.

That is something that should be comfortable so if you come with a good background in quantitative ability that always helps the other bit is programming experience so this is something that'll really help you so you know and essentially if you are C's sort of career in machine learning then you have to get good at Python right so programming experience if you have or if you at least have programming knowledge that you learn through them and you're confident about picking it up that is again something that will help you because that's that something you use extensively the third aspect is you know an analytical thought process because at the core of all problems that you solve right it will always be about solving business problems solving or applications of machine learning which will all go into what kind of problems and challenges are getting solved using machines.

It is really important to have a logical and analytical thought process so that you can think through a problem you can structure the solution and thereby start implementing an answer you know the other extremely important aspect is that you know you should have a knack and like looking towards continuous learning because these are feelings that are evolving every quarter every year right there are new applications and new advances that are being made so irrespective of where you learn from and how to be if you learn you know you can never be in a position where you imagine you've learned everything so you will have to continuously learn right and you will have to have the ability to interpret algorithms you know to understand problem statements to really create solutions.

In terms of skill sets you to know what you what the industry really looks for and what is it that you should be expecting from the program that we run or what is it that you should be looking to gain by joining the PGP ell program at taking Lakes as are the following first and foremost is that a single foundation of data science foundations like this along with Count is is the bedrock of machine learning this is where there's a foundation so this is where everything gets built from then, of course, our deep dive into machine learning techniques you know various techniques are there, however, they use Deena they use what are the pros and cons of each of that rate and understanding its application to industry problem so it's it is doesn't do you any good if you just know clustering from a theoretical standpoint it ultimately you have to know when and how to use it right and that only comes with lots and lots of applications.

The more you practice the more problems and the more data sets that you get your hand and still give it the better you going to get and one tool preferably in data science you know and Python this is the tool of choice when it comes to cheaper all right so that brings me to to the end of our first half rate which is all about careers in machine learning like what to expect what kind of skill sets to build and you know what what is relieving she outlook like what can you expect by doing the program like ours and a job that exists and what does it really mean for you from a professional and personal standpoint.

The next half of this webinar is going to be all around you know the postgraduate program in machine learning which is co-delivered by Great Lakes and in fact in partnership with Stewart school that is Illinois Institute of Technology so here we will take a deeper look into in understanding the program right what is the structure of the program who is the program for a better walkthrough in terms of curriculum what are you going to learn as a beautician of the program how do we ensure that you are industry-ready and you get hands-on exposure which is so valuable in the industry and many more questions like this all right.