DataScience machine learning, deep learning Demo By Depak 9059868766
This, conference, will now be recorded. Myself. I'm, Deepak I have 11. Years of experience, in ID under 8 years into artificial, intelligence, under. Cognitive computing I. Work. For from, the top and minces in India. Okay. So, before that you might have some idea. What. Is data science okay what is that a science or, machine learning or artificial, intelligence, these. All are very, close so in. Our session we will understand, what is in detail, okay. What is it small difference between this but. For now consider, everything is same, okay. But, what is data sense. What. It does, some. Of the applications, okay. If. I want to I'm, a businessman, I want, to predict, my sales okay. Yesterday. Might. Be let's, take a last month I sold. 2,000. Units and. This. Month. 4,000. So. Next, a next, month. Will it be, 6,000. Or. 8,000. Okay 2 into 2 4 or 4, to, blissed also for, ok, 4, plus 2 or 4. Into 2 or, do. I need to consider any marker, strategy, I mean. Let's take a if it is a festival, season. Okay. Machine. Learning or data science considers all this. Parameters. Sales. High because, of, the. Normal trend I mean growth, in my market. Or because. Of festivals. Or something. Related to that so. It predicts. Okay. Sales, how the sales are going okay, what is I predicted, what is my actual value, okay the, red one is predicted, and, blue thing is actual. Value. Okay. On the particular day or particular, month how, my sales will be so. Based on that I'll. Manufacture. That device or items. Okay. NLP. So, everywhere. We are seeing the text okay, we are texting, or we are typing something in Google or we are releasing some complaints, or we're giving some feedback everything. Is a, text so. We need to understand, the text then, only we, can give. Better service people. Are resin tickets so what. Tickets. They're raising okay, might be they're giving some feedback movie. Review okay. What. What they liked. The future delight so for, that we need to understand, that the. Text what they wrote what, their opinion. Okay. And, recommendation. Systems okay, the. Bottom. Left. Even. This. Is captured from Amazon Amazon flick, court Myntra, whatever, the e-commerce, site if. You are looking for one you will get the corresponding items, there, are items related, to that or. If. You open sometimes, the app it. Gives you your, interest, so, it is understanding. And it's, giving the, corresponding item. And. Computer. Vision so. Image processing, okay it's not, just image processing its, understand, the image okay.
Doing. One computer one, image to the computer, it. Is giving me what is there in the image video. Video, analytics, captioning. The image, okay. All, thing, related to, images. Okay. This. All of the fields, which already, implemented. And most, of the. You. Know clients. They already implemented, this is a current, item okay, most of them are implemented, most of them are implementing. Most. Of them are yet to implement. Okay. But. This is the major. That. Three. Okay not, the computer vision majorly but a rest of the three or playing. Major, major, role, in the market. Okay. This is a since, we. Are understanding the data what, is the data it can be text or it can be numeric data, okay. And giving some solutions to business okay. With NLP okay, this is your customers are facing the issues so do something okay. Or. A recommendation, system, so this is a. User. Patterns okay. Suggestion. More more suggestions, more sales more revenue. Okay. The business forecasting better. Forecasting, optimizing. The. Dimension. Of supply chain. Okay. So. This, is some of the items, from. Artificial. Intelligence, and cognitive computing. So. The top left is the, Watson system okay. This, is a quiz. Computation and these two or Ken, and Brad these are the world champions, and at. The in middle we have the Watson, IBM Watson. So. Few. Years back they conducted one quiz and IBM. Watson who owned the price. Okay, this, is just a mission a mission. And. You might might. Have heard about driverless. Cars self-driving cars. Car. Without any. Driver. Okay. This is again the, a artificial, intelligence which. Is clearly related to data sense and, Google. Assistant, even if you are using. Android. Or. Apple. Android. Means Google assistant, and. IPhone. Means Siri so, if you are speaking, something it. Is able to understand, what you're talking about and it. Is searching for you and it is giving the results the. Behind technology is. A-okay. Whatever, the slang you speak, you. Know might be Indian, austin. Us. What are the slang how, fast you speak still. It understands. Okay and charred, boards if. You open, any enough. Flick up little. Big. Websites. I mean the banking, okay if you see HDFC. You see Eva okay. If you see. McDonald's. So it's right so right top. Of sorry bottom. Right you will see one chalkboard. Kind of stuff so everywhere. To. Introduce, their products or give better service to the customer. They, are introducing, short. Board this is again yay and NLP. This. Is cognitive. Computing and the first. One what we discussed about is basic, data sense. Okay. So, this is how the applications. Are everywhere. They're introducing, even if you're in your mobile if, you're taking selfie, the.
Rectangular, Box will come, even. If you are moving here and there still you can see the box is moving towards. Your face some. Of the applications, it identifies. Your gender and mail are sorry gender. And age. This. Is also image, recognization. Okay, so. How the data science market, is not. So this is from one. Of the blog. Not. Blog it's kind of article. Data. Scientist this is a success, job, in 21st, century. Because. Opportunities. Are going everywhere, they're. Implementing, a for. Sure this is a hottest, job. And. This is from Times of India so, fifty thousand pores in that, a sense of vacant in India a report. Still. These are open. Might. Not be fifty, its opportunity. Is increasing, okay. So, more filled and some, are open so. Opportunities, are there for sure. The. Sundar Pichai said, ëi. More, important, for humanity then fire on Alexa T so without end fire on duloxetine we have nothing. As per his prediction, he. Is more important, than this, so, just. Imagine how important, it is it will be for, sure. That. Sense job opportunities, continue, to rise in 2020. This year as well okay this is January or 27. So. AI is improving, so we are doing everything on mobile it's not only because of, software. And Technology, more. Majority, of them are using a artificial. Intelligence, cell break or smart. Cities, everywhere. Area, yeah. Integrating. With the different technology might, be IOT, when. We are talking about a smart, city they. Are integrating, with IOT why with IOT. Okay. And. Demand. Will occupy out. Space at the school supply so. There, is a huge demand. Okay gap between, demand, and supply. I'm. Repeating. The same thing when. I'm saying the supply that quality, supply. Okay. There. Are a number of, people even. From. Almost oh four. To five years I'm interview. Panel member. Okay. If, there. Are. 100. People we might select five people okay. The, only reason is quality. Of education. They, learn somewhere they know something, but. That is not up to industry, standard. Okay, there's a big, big difference. Okay, I'll uh, yeah. This is a major thing okay. The. Day I mean yeah, I mean most of the people okay, very few people work for passion. But. We are looking for salary okay because I'm even, I do, everyone. Because. Of that we're working, right so how, the salaries, will be. This. Is a trend from our 2016. To 2019. Okay. If. You. Have. One. Less than one year experience, you. Will get the, charge is still increasing if, you compare with the Bosch three years it's growing it's a green, line I'm talking about this. Okay, one, to two years also it's increased, in 2019. Okay, two, to five years, it increased, but not great, five. To seven years and seven. To nine years it's, decreased. And at ten to fifteen years and more, than that then. I'm saying less. Than one year this is about. How. Much experience, you are claiming. Okay. Okay we will discuss few things offline once I start my stop. My recording, so. This is about this slide we will discuss again. Okay. So this is about the data. Sense applications. And a, applications. Okay. But, what, is data sense exactly, what. I need to learn, okay. This is a small. But. It. Gives more information with, the, plot. Okay. So. We. Need some computer. Science we need some technology. Okay, technology. Sends something. We need to code we use Python, okay, there is or the recess why we selected, a Python, we will discuss that and. A. Mission learning we discuss, machine, learning 90%. Most of the algorithms which we use in general ok. Math. And statistics we'll. Learn which. Is required, complete. Covering math and such statistics, is not a small. Thing we'll. Learn what is required. For two years experience are two, to three years experience. Ml. Guide. The. Software development so we are developed we will develop ok, one Indian solution, so, how we will receive the data how to build machine learning model how to deploy in production, we will disable, experiment. That we, will do hands-on. Will do practice. Traditional. Research, so. We. Will cover some of the research, papers and how to do the, traditional research, okay. But. I'm not going to cover that domain. And business knowledge okay. Because, it's completely depends. On the, domain. Might be if you're working for insurance. Finance. Retail, it, completely, depends on that so, I'm not going to cover that okay. If. If. Anyone is interested in, the particular domain, we. Will discuss that but not. Mandatory. It's optional you, can connect me offline as well so, where, I've. Worked, I can, explain on from there. Combination. Of all this is a data sense okay. Even. If you are missing the domain. Or, business, knowledge this, is not at all problem. When. You work. In real time still, you can get help from your SMS, okay. If it isn't new, even. I do the same thing even, if some domain is new for me. Medical. Research, still, it is new for me so I learn okay. I'll. Teach, you how to rate, the technology, with. Do not worry okay. Even, I will teach what is typical, data science.
Data. Scientist, day-in day-out okay. How, we receive the data how to sorry. It might be in sorry. Didn't. Next slide. So. By seeing all this might be, with. Math and statistics okay I read, almost two three to five years back or very long may be tougher to news Mac I'm not able to recall. Okay. And algorithms. Okay it's a little less straight vote for me and the computer sense somehow I can manage okay. This. Is fact. Okay. This is might be, funny. Flight. But this is a fact so. When people say machine learning, what. Society, thinks I do so, we are not building, any, robots. We. Are not it's, a kind of you know might, be Robo movie or iRobot, movie we're not making any friendship with them or we, are not doing anything with, sophisticated. Computers, or. When. I'm saying math we are not solving, any. Mathematics. At the core level at least. For. The first two years, okay. But. Actually, what we do we, make use of some libraries. Okay, we're. Do. Not get worried that my main intention, is do not take stress what. We do is they just write a program. But. Yes we need to understand, math, in depth okay. MA understanding, math in depth is different deriving. Massive, difference in depth, ins different V or, understanding. Which, already solved so it's. Okay it's not that much complicated. You. I'm going to cover in this session. Okay. So. Who is a data scientist what is data sent data, sense and what is your rule okay. What you need to do in. Your day, in and day out. Protein okay. Once you switch your carrier what you need to do, ridiculous. Of the course as I. Said I'll cover, required. Mathematics, and the statistics and, a probability. Okay. Do not worry even if you forgot that's. Ok still we will reference. Them. Python. As a programming, language and, required, packages ok. The. Program if you need to code in. Some language right mathematics. Or whatever even if you have some thought process we. Have to code in some language I use. Python, ok. Even. People, prefer. Our but. Still I like Python I'll tell you why and. The required packages as I said, everything. Already coded ok mathematics, or machine learning all algorithms are coded and keeping, in some package I will. Explain those package, ok, it's something related to this the, bottom right if you are seeing my mouse the, bottom rows we, just import, the package from, scikit-learn, the, import is VM with just input and we make, use of them we do not write from the scratch. Ok. That indicates. The required, packages. Ok. So. Then machine. Learning is a core, what. The, met what is, the machine learning will learned it and what kind of algorithms, okay. We'll. Learn in depth machine. Learning okay 90 percent of of the algorithms, which we use very, often I will cover them why. Not 100 percent ratio still, there is a chance for a vowel, so.
A, 10. Percent that might be advanced, very advanced which is not in the market so. Far if. You find. Something in Google ok still I'm okay to explain, that as well okay, but, still. Nice. And today what I am using, or my team is using or my company using, I'll explain them. Okay. Detail, explanation and, implementation. Of ml techniques so not only learning, we. Will do everything hands-on. Okay. In the class I'll. Explain, the, theory, mathematics. And code I'll share the code with you you, have to practice okay the only way is you have to practice come, with the questions we will discuss and we'll sort out. As. I said a day-to-day. Activities, of a data scientist, if you switch your career what, you need to do okay if you or if you have one task how to solve it I will. Explain in. Detail, okay. Real-time. Applications, so after, the course, we. Will implement one, real-time, application, that 2 into n right. From the. Collecting. The data, understanding. The data cleaning. The data. Implementing the algorithm and implementing. I mean hosting dinner from sorrow or real, time we, will see into, end. Okay. That, is also into. End use kiss execution, okay. We will see some of the real-time applications, which already implemented, and we. Implement. One application. And. This, is a reason so most of the people ask me why. Not or why only Python, so. The. Reason is this is from one of the survey on, tidal okay. Which is a very, host, most of the competitions. So. Seventy six point three percentage, or using, Python. And. Most. Of the deep learning. Algorithms. And packages. Available in, Python only, not in our, or. Is very useful, for. Basic. Understanding, and a statistical, purpose, but, Python, is a generic language, you can write the program, you can implement in. A real-time as. One of the friends said we can implement dip, sets as well in herbs at the Django okay. But, if we learn Python we can integrate with everything. But. If you learn or. Statistics. Are really good then, Python. Okay. But, when you are considering. As a path language. Or when you are implementing, in a real-time we face some issues with our okay. Instead, of learning or and some other language, will focus, on Python, which helps. Us into n. Okay these. Are the common kochenko's from, previous batch okay. I'm not. Very good in coding okay, as one, of our friend said I can't. Read optimized code okay. If, you are able to write basic, code okay. If it's conditions, for, loops even we covered that also, in our session. You. Are good okay, you, need not to write some, you know class, of. Object-oriented. Program, or functional program, it basically. One still. I cover those, concept, functional, programming concepts where. You can write. Optimized. Code, okay. We will discuss that you need not to be a. Optimized. Coder or full fledged coder to Jordan. In this course I. Cannot. Understand, math in depth so, machine learning means, it's a math statistics, math, probability, mass right. Everything. Is mass but, I do not understand. Or I do, not recall. I read, in my plus, two or in my engineering. But not now okay. We. Discuss. Which. Is required, in our session I. Do. Not have PhD, so machine learning or data sense means it's I need some certification, or some. Of the. Institute's. Are providing certifications. Okay. So. You, need not to be a PhD, guy, or you know not any. Certifications. In data science okay. Even. If you if, you want some certification, still I can provide some of the areas, where you can participate, and you can get certifications. Today. The. Deeply, I mean data sense. Interviews. How, they're conducting, is they. Are giving a problem line, okay.
They're Giving a problem line if you solve you, will get it that's it there is no certification sell, anything. But. The fact also there. At some. Level you need certification. But they. Also check, for your. What. Is that your. Problems are my skills. Okay. Even, though you have some certificate, still, you have to solve the problem if. You solve the problem in optimized, way yes. You are the chump you. Can code, however, you want how much you want. Okay. I do not have a first computer I have a basic computer with the 4gb still, it's fine we. Work, on, the. Toy, kind of datasets only, where. We work in the real-time data I can, provide you full. First not, provide you I can guide you where. You will get flyff computation, not, only CPU you can get GPO. Under tip you as well I, do. Not have time so I, am working somewhere, I, don't. Have much time so. Should. I what. I suggest is if you have two hours per day mostly. Two, months okay, one hour per. Class. And one. Hour for. Practicing. You are good to go okay. I do, not know the big idea so I, heard a. Machine. Learning data sense but, I do not know what. It is exactly, okay. By the end of the course I'll make. Sure you will understand, into, n ok what, is data science, where. You can you. Know, implement. This in your organization, if you're working somewhere, ok. I will teach you into, n. Ok. Any, questions. Or. Any expectations, I'm stopping the recording so that we can discuss, freely.