What is the connection between artificial intelligence & machine learning?

Applications in these areas and they’re getting better over time super is futuristic sooji right now only exists in Hollywood movies and fiction novels and the fact one of the quotes that I really like about you know this whole debate about whether AI will take over the planet in their arguments would be delegated as slaves were given the Andrew ng one of the foremost thinkers on this area and he mentioned it that he worries about soup earlier in the same way that he should worry about overpopulation in Mars right.

It is that far Thornton right but let that not distract you from the application and the reality of AI that surrounds all of us today on that note let me take you one level ahead one level deeper right and let me try and explain to you three terms that are often used interchangeably and often quoted out of context right but one of the things that I thought I should try and do is also demystify some of these since we all right.

So we’ve spoken about a yeah that what does AI really is essentially you’re talking about developing algorithms computing systems and machines in such a way that they can understand context understand data and give output whether that is inside whether that is information any of the output that the system is supposed to give at a rate that is comparable to humans or at an accuracy rate its competitive with comparable to humans or slightly better than humans right so that is the broad new world of AI now there are two other associated terms one is machine learning and the other is deep learning first.

Let’s talk about machine learning so machine the science of machine learning’s it’s a complete field of computer science right so it’s an entire field of study and that is something that we all want you to appreciate right it’s a very vast and deep field the field of machine learning is all about creating algorithms using certain techniques in such a way that you can train these algorithms over the period of time as more data gets ingested right so the algorithms start learning and start becoming smarter thereby their output starts improving over a period of time and machine learning and AI are very iterative because machine learning is one way of implement in Ai

Mlis a set of techniques to implement AI right.

The connection between the two so obviously the question that may come up in your mind is that can you do AI without a machine gun and the answer the theoretical answer to that is yes you can write it you can implement it without machine learning but implement AI without machine learning the effort and time and investment that’s going to go in you know commune maintaining millions of lines of code and constantly updating it and you know deploying it is a lot of lot more than that is why it is not feasible which is why the only practical way of implementing AI is through machine learning right.

You really cannot advance an AI unless you know machine learning because that is the only way that hey I will get implemented in a scalable and sustainable fashion right because as I said what you’re trying but the systems that you’re building the engines that you’re building by using machine learning are in such a room okay that they improve beautiful times let your effort does not increase non linearly as the scale of data increases then it’s not sustainable right which is my machine learning is important to machine learning then further breaks down into multiple categories whether it is suitable earning unsupervised learning reinforcement learning all these are a bucket of technique is depending upon the problem you’re trying to solve but all of these are means to an end and the end is essentially implementing in a burrow di solution all right so what is important for you to understand is that machine learning is a way or a means of implementing AI.

What is deep learning is an alternate set of techniques that you will deploy at some point in time when you want to take the accuracy of your system even higher when machine learning models can only take it to a certain scale and then you deploy deep learning techniques to increase it even further and I’ll explain that with an example but the broad difference here is that deep learning the whole science and field of deep learning is all about creating algorithms of creating systems in such a way that they mimic the human brain right and obviously there is a lot of science behind that like when you think about the human brain you know that its capacity to analyze to consume information to give insights is practically limitless.

It can do all of this gathering the context that exists around it right so deep learning is when you are actually trying to build solutions right when you’re trying to build recruiting system in such a way that you can enhance what machine learning has already achieved and you’re trying to take it even further some for every problem that you face it is not as if you need to build a solution using Duplin but it really depends upon the level of accuracy and the complexity of the problem the level of accuracy that you want to achieve and the complexity of the problem that you’re trying to solve in today’s world there are a far there are far more applications of machine learning and that’s why there are so many jobs in machine learning and deep learning is something that is now beginning to kick in because for a lot of these organizations that are implementing machine learning solutions.

They’re one of their focus areas is also how do you enhance on top of that and that is when you know areas such as deep learning come into prominence let me go back to the example that I was explaining to you which is image recognition you know the deep freeze algorithm by Facebook right so image recognition is typically about the first you know examples or case studies or walkthroughs that you will come across when you begin your journey in learning machine learning it’s a classic machine learning problem so when Facebook.

Now if he came back about the journey that you know the machine learning engineers in the air engineers would have made at Facebook to bring the face to life obviously what is the problem that they’re trying to solve the problem that you’re trying to solve is that they have to build an algorithm that recognizes human beings based on pictures that they have right now typically when you try to solve such a problem it is not as if we immediately jump into one solution and tribal to start building it right.

You will first start building a model on a smaller data set that gives out a certain set of results in a certain at certain predictability right now go back in time and all the times when they were actually tagging people manually what you were doing was that you were training the algorithm the data you were saying that with this picture belongs to this friend of mine who’s by this name right and this other picture also belongs to this friend of mine who’s by the snake right.

Here you can learn all the techniques of Machine Learning & Artificial Intelligence.

What we help them with is defining the scope of the problem defining the approach and all of that right but with that, you can build a portfolio of projects that you of the problem that you want to solve great that is very evolving and contemporary.

These three things what we make sure is that the learning outcomes what people get a tender program are wonderful right and that also translates into really good career outcomes for our students another man okay and we covered all the techniques which are using the machine learning and AI all of them are covered in the course right so deep learning neural networks right we use Python Python is the language of choice for especially for AI algorithms like TensorFlow etc developed are developed in vitam we cover all the basics plus you know.

We don’t need you to be proficient in Python during the course we ensure that you build a base in Pike ton and then start using it so so there is no expectation for you to come coming into the course for you to have a prior understanding of Python right and the other thing is known focuses in addition to having theoretical concepts covered but so that you have a good appreciation and understanding of all of these techniques he focuses also and actually more on making sure that these are married to the applications right to your business understanding so that you can use these techniques to solve real business problems, so this is more of practitioners perspective that you collide and then you have a portfolio well plus in the sea projects this is the structure of the program right we start with a little start a little slow by building a base in Python and stats then we go and covered in machine learning techniques.

In the AI we come to cover all the relevant use cases and deep learning using your networks which are computer vision NLP intelligent agents it’s a girl thing and the AI part of the course has been created by IIT Bombay professors for us they again there’s some examples of the kind of projects which our students have done looking at let’s say New York City’s taxi supply using looking at data from uber like some of these from which the company still uber is trying to solve and kind of evolving problems is what we usually do as capstone looking at cash customer sentiment forum is on reviews like all of this we just give examples on how our projects predict our structure than they’re always on the cutting edge on the things that and temporary problems with industries also trying to solve eight, so these are the prerequisites for the program and I get a lot of questions on this that do.

We know do we need to know Python do we need to know programming language programming before a friend you know signing up for this course you know the simple answer there is as long as you’re not scared of programming right then you are good because whatever you need to know and Python is a very easy language to learn right it’s unlike like C C++ where you have to write lines of codes Python is a very convincing language with like two-three lines of code you can not an evil network it’s very very easy user friendly and easy to use and very easy to learn right so so you don’t need to know but you should definitely not be scared from writing up some lines of codes.

So that’s the prerequisite of the program right and these are some of the other things that we do to ensure there is career success for each participant so we do a lot of career development workshops right where we talk about what emerging trends what kind of roles can you look to apply for what can a force exists right what kind of transitions people have made what kind of skills do you need to have all of this right industry connect through mentors who are in a lot of we also do career fairs at regular intervals every month lasts a career favorite it was in Chennai where about 20 companies came in on the same day and about 200 alumni attended that very affair and 140 of them got you to know further letters of intent from that process.

We keep doing it and all our alumni and students invited us to all of these career fairs like peer to peer networking we keep publishing opportunities lateral opportunities on our accelerate platform right so all of this is this ecosystem this support system also is in place to help you once you have achieved learning outcome to also help you get the career out comparing self okay and if you look at this list where our alumni work what you realize is that now and this is only an indicative list right I’m guessing any major organization we have a relevant I’m working there and that too at a fairly you know mid to senior role in ml Nazi domains right and what you’ll see here is that the companies are secure are not particularly are not limited to only some domains.

It’s like 10 years ago it used to be only technology big technology companies like Amazon Facebook Google right but now you see things companies like oil and field right Todd’s Apollo Hospitals so across sectors now there are opportunities and machine learning and AI which are across sectors and as I was talking about this is more a broad-based opportunity which is evolving like and this is the usual the kind of analogy which I take here is you go back to 1990s right where the whole idea revolution happened it started with only a few companies but within five-six years every company was utilizing it.

Now you cannot imagine a company that is not utilizing you know complex IT systems similarly for this within ten years it will be unimaginable for any company not using machine learning or AI, so that was all for me let me just see if there any questions okay let me just quickly go through some of the questions, yeah, so one question is if you go to 99% accuracy with deep learning does it not over for it right, so there is the way this deep learning techniques are defined the going to define are very centered on the outcome that they have so that ensures there is no overfitting there right and so some of these details yet you will learn in the program late can I understand the DL is the enhanced model of ml.