[0:00]Hello guys. Welcome to my YouTube channel. This is 100 days of machine learning and today is day eight. Aaj hum log padhne wale hain kuch important applications machine learning ke theek hai. So kafi cheeze pad li abhi tak humne. Uh what I want is ki coding wala part start hone ke pehle mathematics wala part start hone ke pehle, main aapko inspire kar paun ki aapko sach mein machine learning padhna chahiye. Theek hai? So, if you see around yourself, aapko already bahut sare aise examples milenge software products ke jahan par machine learning already integrated hai. Theek hai? Matlab Facebook pe friend recommendation, YouTube, Amazon pe product recommendation, video recommendation, I don't know chatbots, or pachaas aur cheeze yaar matlab I can't even remember right now, but agar main mereko I am confident ki is point pe agar main baith ke sochne lagu I can write down hundreds of examples jahan pe machine learning ek product ke andar use ho raha hai. Theek hai? Toh, it is something jo future ki baat nahi hai, it is already in our life. Theek hai? Already hum usko use kar rahe hain aur itna normal tarike se use kar rahe hain ki hum realize bhi nahi kar paa rahe hain. But generally as students, what what we think of machine learning is it is in terms of consumer products. Hum jab bhi sochte hain machine learning kahan use hota hai, toh hamara dimag hamesha hume aise products ka example deta hai jo bahut straightforward users use karte hain jaise ki WhatsApp, YouTube and all. What I want is ki main aapko thoda B2B applications batana chahta hu. B2B ka matlab business to business jahan par machine learning ek product mein ghus kar ke ek business ko help kar rahi hai apna business run karne mein. And trust me, B2C, business to customer jo normal applications hote hain usse bahut jyada paisa kamane wala cheez hota hai B2B. Toh I would like to show you ki B2B mein kaise aapka machine learning companies ko help kar raha hai aur profit kamane mein. Kyuki B2C examples to be honest aap thoda bhi YouTube karoge toh aapko bhar ke mil jayenge. That is why I wanted to I want to give you a B2B example theek hai? Toh maine paanch alag alag sectors ke baare mein baat karni hai. Kyuki machine learning ek aisi technology hai jo alag alag sectors ko impact kar rahi hai. Toh I'll talk about retail, I'll talk about transportation, manufacturing, banking, and social media jo bahut famous hai aaj ke date mein. But trust me in paanch examples ke alava bhi aapko bahut sare aise examples mil jayenge jahan pe machine learning B2B mein help kar raha hai companies ko profit earn karne mein. It could be space exploration or medical field ya phir defense in sari jagaho pe bhi machine learning use ho raha hai. But ye jo paanch examples main de raha hu ye bahut interesting hai matlab aapko maza aayega inke baare mein sun ke so let's start. Okay. So I'll start my discussion with the retail sector.
[3:08]Retail mein machine learning bahut jyada use hota hai. In fact, retail mein agar machine learning use na ho, toh it would be quite difficult for these companies to run properly. Theek hai? I'll give you some examples. Retail mein we can take an example of Amazon jo ki khud mein ek bahut bada e-commerce website hai jahan pe wo bolte hain ki 6 crore se jyada products bechte hain wo log. Ab I'll show a few scenarios jahan pe machine learning use ho sakta hai. Ab mujhe nahi pata ki exactly wo kaise use kar rahe hain. Let's say aap Amazon ka har saal ek sale aata hai, Great Indian Festival bol ke. Ab you think about this ki agar aap Amazon ke head hote, at least operations ke, toh sale mein aapka uh you know products ka sale badhta hai, sabko pata hai hi, that is the reason why people conduct sales. Toh agar aapke paas 6 crore products hain, toh kya aap 6 crore ke 6 crore products ka stock badha doge ya nahi badhaoge? Obviously, sare products ka stock badhana bewakufi hai. Bahut jyada paisa lagega and there is no guarantee ki aapke sare products jyada bikenge. Toh it's very very important ki aap ye pata karo sale hone ke pehle ki kaun se products jyada bikenge. Aur is tarah ke scenarios mein aapke jaise data analyst, data scientist logo ko bithaya jata hai. Pichle kai saal ke sales ka data diya jata hai, ye jo great Indian jo bhi hai iska sale ka data aapko diya jata hai. Aur aapka kaam hota hai machine learning algorithms run karke, data mining karke, is tarah ke insights nikal ke do ki ye product ka stock up karna chahiye, ye product ka stock up karne ka zarurat nahi hai. Theek hai? And trust me, socho agar aap isme thodi bhi galti karte ho toh karodo ka nuksan ho sakta hai. Theek hai? Toh e-commerce jitni bhi websites hain, Myntra, Flipkart jo bhi websites aap use karte ho, wahan pe agar sales conduct ho rahi hain, toh un sales ke conduct hone ke pehle aapke jaise engineers baith kar ke ye decision making karte hain ki kin cheezo ka stock badhana chahiye, kinka stock nahi badhana chahiye. Theek hai? I'll give you one more example jahan pe retail mein machine learning bahut use hota hai. I hope aap Big Bazaar kabhi na kabhi gaye hoge ya Spencers gaye hoge kabhi na kabhi. Aapne hamesha notice kiya hoga ki wahan par aap jaise hi saman kharid ke apna bill katane jaate ho ya payment karne jaate ho, toh the first thing that they ask you is aapka phone number, right? This is so weird right? Agar main road side kisi thele se sabzi kharid raha hu, toh main sabzi kharidne ke baad agar usko paise dun aur wo paise lene ke pehle mujhse puche aapka phone number kya hai, toh main gussa ho jaunga. Right? Main usse kabhi sabzi nahi kharidunga. But these companies they ask for our phone number aur hum de dete hain. Right? Now you might be thinking ki haan because wo log palat ke hume SMS karte hain ki naya kuch sale hai ya phir kuch offer chal raha hai, nahi uska reason ye nahi hai. Uska reason ye hai ki wo track karte hain aapke buying behavior ko. Theek hai? So what they do is aap jo bhi cheeze kharid rahe ho, let's say aap I don't know milk products bahut kharid rahe ho, aap health related cheeze bahut kharid rahe ho, toh they would see there is this buying pattern ki aap health conscious ho. Theek hai? Similarly koi aur agar bahut jyada spicy cheeze kharid raha hai ya phir I don't know sports related cheeze kharid raha hai ya bahut cosmetics related cheeze kharid raha hai, toh what they do is they create profiles of their customers. Since pure India mein Big Bazaar hai, Spencers hain. Toh aapka ek ek kind of uh ye bataya jata hai ki ye kis tarah ka customer hai aur uske interests kya hai. Theek hai? Aur phir kya hota hai ki aapka ye number dusri companies ko becha jata hai. Right? Matlab aapne notice kiya hoga aapko bahut bar aise aise SMS aate hain unknown number se ki ye gym join kar lo ya phir ye sports ka kuch chal raha hai join kar lo ya phir ye classes chal rahi hain join kar lo.
[7:08]Unke paas aapka number kaise pahuncha. Bahut log hain waise aapka number bechne wale.
[7:15]But ye log bhi hain aur ye log aur jyada paisa mangte hain advertisers se because inke paas aapke baare mein bahut important crucial data hai ki is bande ko for sure uh gym membership chahiye hai kyuki ye health oriented hai. Kyuki soch ke dekho na agar gym wale jaa kar ke 1 lakh logo ko aise hi randomly bhej de SMS toh conversion bahut kam hoga. Wahi agar wo Big Bazaar se jaa kar ke data kharid rahe hain aur Big Bazaar wale unko sirf 100 logo ka phone number de rahe hain jo sach mein bahut interested hain health related products mein toh unka conversion rate would be great. Toh unko 1 lakh SMS karne se jitne results milne wale the utne results unko sirf 100 SMS karne mein mil jayenge. Toh basically targeted marketing ke liye aapka profile create kar rahe hain ye log. Yahi same kaam Google bhi kar raha hai, yahi same kaam Facebook bhi kar raha hai. In fact agar aap koi bhi product use kar rahe ho jiske liye aap pay nahi kar rahe ho, most of the internet products we use but we don't pay for, toh uske baare mein ek famous cheez kahi gayi hai. If you are not paying for the product, then you are the product. Toh ye cheez yahan pe bhi true hai. Aapka data sach mein becha jata hai, theek hai. This is the second scenario jahan pe retail mein machine learning bahut use ho raha hai. Yahan pe aapka buying pattern dekhne ke liye. Third scenario is this. Aap ye photo dekho apne screen pe. Yahan par you would see ki kuch products shelves mein rakhe hue hain, aisles mein. Theek hai? Ab jab main pehli bar gaya tha, pehli bar nahi but main kabhi bhi jaata tha pehle teen char saal pehle Spencers ya Big Bazaar mein, toh mera ek mera andar ek curiosity aata tha ki kaun decide karta hai ki sare products ka positioning kya hoga. Matlab mujhe kaise pata ki is product ke bagal mein ye product hi hona chahiye. Apparently ye cheez decide karne mein bhi machine learning ka bahut use hota hai. There is a thing called association rule based learning jiska exact kaam hai ye karke dikhana. So aap do products ke beech mein correlation nikalte ho. If the correlation is very high, aap un dono products ko sath mein rakhte ho. Maine kuch din pehle aapko example diya tha baby diapers aur beer ka. That was the exact same example. So you can see agar ye teenon cheezo mein aap machine learning ko hata do toh aap vapas aadi manav wale zamane mein chale jaoge aur ye companies utne ache se function nahi kar payengi jitne ache se kar sakti hain. Right? Toh machine learning is a very key component in e-commerce retail sector. Theek hai? Now, let's move on to the next sector, that is banking and finance. So banking mein, I don't know aapne agar kabhi loan ke liye apply kiya hai, toh aapko pata hoga ki sabhi ko loan nahi milta. Loan agar aapko chahiye hai toh pehle aapko apna profile submit karna padta hai. Aur wo profile ke upar pehle ek analysis hota hai. So do level analysis hota hai generally, ek ek machine learning algorithm ke through hota hai aur phir ek manual koi loan officer ke through hota hai. Toh jo machine learning algorithm wala stage hota hai wahan pe kya hota hai ki aapke profile ko compare kiya jata hai past defaulters se, jin logo ne loan nahi chukaya.
[10:39]Agar aapka you know similarity bahut kam aa raha hai 10, 15, 20, then that application is passed to the loan officer jahan par wo ek manual processing karne ke baad aapko loan sanction kar deta hai. Theek hai? Toh banking mein aur bhi bahut sari cheeze hain jahan pe machine learning use hota hai. Kahan pe branches kholne chahiye, kahan pe customers ke liye kaun sa promotion plan start karein, kis tarah ke plans hum cities mein start kar sakte hain is tarah ki bahut sari cheezo ke upar machine learning implement hota hai. Toh finance is again one thing. Finance mein actually bahut sari aur cheeze hain. There is insurance, there is share market. Ye sare sectors mein aaj ki date mein machine learning trading and all in sari cheezo mein machine learning aaj ki date mein bahut use hota hai. Theek hai? Let's move on to the next sector, that is transportation. And I'll give you an example of Ola. Uh again bahut sare examples hain isme bhi, but main aapko ek example deta hu Ola ka. So uh main jab Kolkata mein rehta tha, toh us time pe hi Ola start hua tha Kolkata mein, it was I guess 2015 I guess. Toh tab ek concept aaya tha search pricing ka jo abhi bhi hai ki kuch specific times times pe subah ya sham ke time aapka jo pricing rehta hai, jo fair rehta hai wo normal se jyada rehta hai, kabhi kabhi double rehta hai, triple rehta hai. Weirdly jyada, right? Toh mereko bahut surprising lagta tha, mujhe gussa bhi aata tha Ola ke upar ya Uber ke upar ki yaar is tarah ki pricing aap kaise kar sakte ho. Agar mujhe point A se point B jana hai aur uska actual fair is 100 rupees, how can you ask for 300 rupees? That is so unfair, right? Phir ek din mujhe is cheez ka answer mila ki Ola aisa kyu karta hai ya Uber aisa kyu karta hai. So I was sitting with this driver. Aur uske phone mein Uber ka Uber ka tha ya Ola ka tha I don't remember but uska khud ka ek driver app tha. Aapka do apps hote hain Ola aur Uber mein, ek hota hai user app jo hum use karte hain, ek hota hai driver app jo driver use karta hai. So mujhe uske driver app mein aisa map dikhayi diya Kolkata ka. Usme se kuch regions is tarike se red the. Toh out of curiosity maine pucha ki bhaiya ye uh matlab dada, dada bola maine Kolkata mein we call dada. So dada ye red regions kya bata rahe hain? Toh he said ki Uber agar main agle 10 minute mein is red region mein pahunch jaunga, toh wahan se agar main koi pickup leta hu, toh mereko Uber 2 times paisa dega. Right? Tab mere dimag mein click kiya ki acha ye cheez kaise kaam karti hai. I guess aapko samajh mein aa gaya hoga. Ab let's say ye area office area hai aur abhi sham ka time hai matlab log office se nikal rahe hain. Ab is point pe yahan pe sirf teen cabs hain, let's say, but customers bahut sare hain, wo app pe aake book karna chah rahe hain. Toh suddenly se ek aisa region ban gaya jahan pe demand bahut jyada hai, supply bahut kam hai. Right? Toh ab Uber ko agar apni supply ko maintain karna hai, toh usko aas paas ke cab drivers ko yahan pe bulana padega. But aas paas ke cab drivers kyu aayenge. Right? Wo extra distance travel karke kyu aayenge? Toh ek hi tarika hai unko lalach de ke bulane ka ki unko extra paise diye jayein. But wo extra paise Uber apni jeb se dega toh bahut jaldi company band ho jayegi. Toh that is why wo extra paisa jo lalach dene ke liye driver ko bola jata hai, Uber jo deta hai wo actually aap se charge kiya jata hai. Theek hai? And that is why they say this ki prices are higher because of increased demand. Theek hai? Toh ho kya raha hai wo supply maintain rakhne ke liye Uber is doing this ki aas paas mein sare jo drivers hain unko ye notification deta hai. Yahan pe dekho, yahan pe bol raha hai ki agar aap is region mein pahunchoge toh 3.2X milega aapko. Agar aap is region mein pahunchoge toh 1.8X milega. Theek hai? Toh that is how this entire thing is uh created, right? Uh demand forecasting kiya jata hai jaise Kolkata mein Durga Puja mein bahut jyada demand rehta hai cabs ka. Toh kis point pe kaun se jagah pe kitne cabs hone chahiye aur uske hisab se kaise redirection karna hai. Is tarah ki cheezo mein bhi machine learning bahut use hota hai. Theek hai? Aaj ki date mein logistics mein jitne bhi startups hain, delivery ka routing and all, ye sab kuch ke liye machine learning bahut use hota hai. Theek hai? Swiggy apne ek bande ko teen orders pakda ke bhej raha hai. Ab un teen orders ko sabse optimized tarike se kaise pahunchaya ja sakta hai, iske upar kaam chalta hai. Google Maps mein bahut sari cheeze ho rahi hain. Toh transportation sector mein logistics mein bhi bahut kaam chal raha hai machine learning ke upar. Theek hai? The next one is manufacturing. Uh so manufacturing mein main aapko Tesla ka example dunga. Although ye example kisi ke upar bhi applicable hai. I'll give you this example because uh Tesla is one of the most advanced companies jo apne automation mein bahut matlab apne manufacturing mein bahut jyada automation use karta hai. Matlab ye Tesla ki factory ka ek image hai jahan pe bahut sare automated arms, robotic arms mil kar ke car manufacture kar rahe hain. Theek hai? Ab is tarah ke scenario mein kya hota hai ki jaise Tesla ke bare mein agar aapko pata hoga, Tesla ka fan following waisa hi jaise Apple ka hai phones mein. Toh Tesla ka car aata nahi hai ki uske pehle hi booking start ho jati hai. Matlab Tesla ki booking aap aaj karoge, probably 6 months baad aapko delivery hoti hai. That is how Tesla works. Toh Tesla ab since aapne bahut sare logo se paisa le liya hai toh aap hamesha ek tight schedule pe cars manufacture kar rahe hote ho. Ab let's say in robots mein se koi ek robot kharab ho jata hai, let's say jiska kaam hai engine bithana, gadi mein engine ko bithana, wo robot kharab ho jaye. Toh us din Tesla ki factory mein kitni cars banengi? Ek bhi nahi. Kyuki gadi mein engine ki zarurat hai. Right? Toh ek bhi car nahi banegi aur aap ek din schedule ke piche chale jaoge. Aur as Tesla jo 6 mahine pehle se paise utha chuka hai, wo ye nahi kar sakta ki wo delay kare delivery mein kyuki phir bahut gali khayega, feedback kharab hoga aur bad word of mouth publicity hone se koi bhi company darti hai. Theek hai? Toh Tesla jaisi companies kya karti hain ki wo apne in automotive robotic arms mein IoT based sensors lagate hain. Internet of things based sensors. Jo constantly aapke in devices ko monitor karta rehta hai. Uska temperature, RPM, pressure, I don't know, jo bhi metrics hote hain wo pure time capture ho ke ek server pe aate rehte hain. Ab let's say koi ek device mein fault occur kar raha hai toh fault aisa nahi hai ki turant occur karta hai. Fault gradually occur karta hai. Toh let's say aapka RPM 300 se ghat ke 299 ho raha hai, phir 298 ho raha hai, 295 ho raha hai. Toh this is a signal ki aapka us device mein fault occur kar raha hai. Toh jaise hi aap ye detect karte ho ki kuch fault occur kar raha hai, aap turant apne engineers ko raat mein bhejoge aur us particular robotic arm ko theek kar doge. Theek hai? Toh isko bola jata hai predictive maintenance. Maintenance aap generally kab karte ho? Cheezo ke kharab hone ke baad. Predictive maintenance ka matlab kya hua ki cheezo ke kharab hone ke pehle hi aapne prediction kiya ki cheez kharab ho sakti hai aur aapne jaa ke usko maintain kar liya. Right? Toh this is again one example jahan pe machine learning is kind of transforming the entire sector. Ye jo manufacturing sector hai bahut badhiya wahan pe kaam chal raha hai. Theek hai? One last example, this probably is the most interesting example of all of these. Uh consumer internet and the example is Twitter. Theek hai? So Twitter is one of those companies jo uh bahut salo se hai. 2008 mein start hui thi aur But it was one of those companies jo bahut profitable nahi thi. Matlab bahut jyada paise nahi kama rahi thi, in fact paise hi nahi kama rahi thi bahut salo tak.
[18:24]Toh Twitter ke jo investors the jin logo ne paise diye the aur shares kharide the Twitter mein initially, wo logo ko thoda dar lagne laga ki Facebook teen char saal mein paise kamane lag gaya tha, Google do saal mein paise kamane lag gaya tha. Twitter paise kyu nahi kama raha hai. And at one point logo ko dar lag raha tha ki Twitter band ho jayega because there is no active source of revenue. Toh us point pe Twitter ke jo bhi heads the mujhe exactly nahi pata ye blog mein padha maine kisi blog mein. I don't have this blog ka detail nahi hai as in iska link nahi hai, otherwise main share karta aapke sath. But maine kahi ye puri cheez padhi hai. Uh ki Twitter ke jo sare heads the un logo ne mil ke phir ek plan churn out kiya ki what they will do is they will start utilizing their tweets. They will do sentiment analysis on the tweets and then they will, you know, use a plan to earn revenue from that sentiment analysis wala cheez. Ab agar aap beginner ho toh probably aapko sentiment analysis kya hota hai shayad pata na ho. Toh what I will do is I'll first show you ki sentiment analysis kisko kehte hain aur phir main aapko batata hu ki sentiment analysis ke aas paas kya plan banaya Twitter ne ki wo aaj ki date mein ekdam mast profitable hai. Let me show you. So if I go to my browser. Ye ek website hai jo mere ek student ne banaya tha. Maine project diya tha. Toh they he built this website. Is website mein agar aap koi bhi movie ka naam search karo, toh ye website kya karta hai ki IMDB pe jaa ke us website us movie ke jitne bhi reviews hain, un sabko collect karke hamare paas le aata hai. Aur phir each review ko padh kar ke ye decide karta hai ki wo review ka jo content hai wo positive hai ya negative hai. Matlab us review ka jo sentiment hai jisne bhi wo review likha wo positive likhna chah raha hai ya negative likhna chah raha hai. Ab ye khud mein bahut important cheez hai. Jab aap natural language processing padhoge wahan par you would understand ki sentiment analysis is something jo bahut use hota hai aaj ki date mein. Theek hai? Chatbots waghairah mein bahut use hota hai. Ab main kya karta hu main aapko ek movie ka dikhata hu. Uh let's say Dunkirk bol ke ek movie hai Christopher Nolan ki 2017 mein aayi thi. Main ne search kiya is movie ko. Ab apparently uh IMDB mein do alag alag movies hain, ek 1958 mein aayi thi aur ek 2017 mein aayi thi. Main 2017 wali ki baat kar raha hu. So main ne 2017 wale pe click kiya. Ab thoda sa time lag raha hai kyuki main sare reviews fetch karke la raha hu aur phir processing kar raha hu ki uska sentiment positive hai ya negative hai. Ab main ne phir ek rating bhi kiya. Number of bad reviews divided by number of bad reviews plus number of good reviews. Toh mera rating score is coming out to be 2.8. Although Dunkirk ka rating IMDB pe 7.8 hai. Ab yahan pe dekho. Jo bhi green text dikhayi de raha hai wo mere system ko lag raha hai ki positive review hai. Aur jaise ye pehla wala dekho. Brilliant cinematography, real planes flying, real destruction of planes, the most real as the most real as possible. Jo bhi hai padh ke lag raha hai ki banda tareef karna chah raha hai. Aur mere algorithm ne detect bhi kar liya ki ye positive sentiment hai. Ye sab positive sentiments hain. Ab yahan pe dekho. Agar main ye wala padhu, I was very disappointed with this movie. I have liked previous Nolan movies but this one left me feeling cheated that I had spent that much money to see it. Ab ye padh ke lag raha hai ki banda dukhi hai aur mera text red color ka aa raha hai jo bata raha hai ki mere algorithm ko ye samajh mein aa gaya ki banda ka sentiment is bad. Right? Similarly this one put down the cool-aid. Theek hai? This one. Ye phir bhi theek hai. Christopher Nolan is a visual genius. This film is just stunning to look at. Positive. This is negative, negative and so on. Theek hai? So this is called sentiment analysis and Twitter ne sentiment analysis ko use karke ek bahut mast plan banaya. Wo main aapke sath discuss karta hu. So I'll take an example matlab I'm not saying ki exactly aisa Twitter ne kiya, but I'm just giving an example ki sentiment analysis ko use karke Twitter ne kaise profit kamane ka plan banaya. So let's take an example ki elections ho rahe hain, theek hai. India mein elections ho rahe hain. Let's say West Bengal mein bhi elections ho rahe hain aur let's say ek trend ho raha hai Twitter pe. There is this trend ki West Bengal elections 2021 jo aajkal bahut chal raha hai.
[23:03]Toh ab Twitter kya karega? Twitter jitne bhi us hashtag ke andar tweets hain, un sabko collect kar lega. Sabko collect karke ek taraf le aayega jaise humne sari movies ko ek taraf le aaye sare reviews ko ek taraf le aaye. Ab Twitter question puchega ki Mamata Banerjee jeetegi ya Narendra Modi jeetenge? Question ye hoga ki Narendra Modi ke liye tweet positive sentiment le ke baitha hai ya negative sentiment le ke baitha hai. Theek hai? Ab man lo ki aapne jaise sentiment analysis kiya, man lo aapke paas 50,000 tweets hain. Ab 50,000 tweets mein aapka 40,000 tweets bol rahe hain ki Narendra Modi jeetega. Let's say, theek hai. Ya Narendra Modi haarega kuch bhi hai. Theek hai? Toh aapke paas ab ek real sort of data hai ki log kya soch rahe hain election ke bare mein. Right? Now there is a great chance ki yahi actual result ho. Right? Toh ab agar aap Twitter ho aur aapke paas ye data already hai ki Narendra Modi ke jeetne ka probability 60% ya 70%. Toh aap kisko ye data sell kar sakte ho so that aapko palat kar ke maximum profit mile.
[24:54]Jab bhi main ye question class mein puchta hu toh students hamesha media houses bolte hain. Aaj Tak ko becchenge, India Today ko becchenge. Aur main hamesha bolta hu galat hai kyuki sahi jawab hai aap stock brokers ko bechoge ya stock brokering jo companies hoti hain Morgan Stanley ya JP Morgan is tarah ki companies ko aap bechoge jo stock market mein paisa lagate hain amir logo se le ke.
[25:43]Toh ab is point pe let's say Reliance ka share is 20 rupees, mujhe nahi pata 20 rupees aur khoob sare shares main ne kharid liye. Theek hai? Ab election hua, sach mein BJP ka government bana. Toh ab logo mein ek sentiment aaya ki boss BJP ka government bana hai. Toh ab Reliance upar jayega. Toh jo log stock market trading karte hain wo sab jaa kar ke ab kiska share kharidna chahange? Reliance ka. Ab since khoob sare log share kharidna chahange toh share ka price badhne lag jayega. 80, 100, 120 ke chala gaya. Mujhe kuch idea nahi hai share market ka, just saying. Ab jaise hi price upar badhne lag gaya toh share kiske paas hai? Mere paas kyuki maine pehle se kharid rakha hai. Toh ab main kya karunga higher prices pe un shares ko bech dunga. Theek hai? Aur mujhe kya palat ke mila? Profit. Ab ye profit mein utha kar ke us bande ko dunga jisne mujhse paise liye the share market mein lagane ke liye aur iska ek thoda sa hissa main jaa ke Twitter ko dunga because mera jo bhi data intelligence aa raha hai that is coming from Twitter. Right? So ab ye paisa thoda nahi hota by the way. Ye karodo mein hota hai, probably arbo mein hota hai, mujhe koi idea nahi hai. But this was a very smart technique ki aapke paas khoob sara jo data hai pure duniya bhar ka. Uske upar aap sentiment analysis laga ke sahi questions puch ke you can actually create very good repository of human intelligence jo aap alag alag companies ko jaa ke bech sakte ho. Maine aapko sirf politics ka example diya. Ye same example extend kar sakta hai sports mein, entertainment mein, kisi bhi cheez mein. Aur ye sirf India ka example hai, duniya mein 200 se jyada countries hain. Toh imagine Twitter agar intelligently apne data ko uh you know use kare toh kis level pe cheeze achieve kar sakta hai and that is actually true. Mujhe jaise hi is blog ka link milega main aapke sath share kar dunga. Is point pe I don't have any proof ki aisa kuch hua. But yeah, sentiment analysis use karke aaj ki date mein consumer internet companies they are actually earning a lot of money. Theek hai? Toh the whole idea of this video was guys ki main aapko inspire karna chahta tha ki ye ek aisi technology hai jo companies ka bhavishya change kar de rahi hai. Toh aap apne bare mein agar socho, toh aapke life mein bhi ek is tarah ka transformation la sakti hai. Right? Toh ab since hum log dheere dheere aage badh ke ab actual parts mein ghusenge mathematics mein, codes mein, toh wahan pe kya hota hai ki thoda thoda beech mein kabhi kabhi kuch difficult lagta hai toh aap thoda sa motivation lose karne lag jate ho. Toh ye video aap yaad karna ya dekhna toh aapko lagega ki agar main seekh jata hu ache se toh I'll be a very valuable professional to any company ya phir agar main khud ki apni koi company khol raha hu toh mere paas ek bahut powerful tool hai jisko use karke I can actually build products jo samne wale ka life badal sakta hai. Toh yeah, that was the whole idea behind this video. I hope you are liking this entire playlist. Hum log aathve din mein pahunch gaye hain aur I'll I'll just try ki hum dheere dheere dheere dheere karke pura playlist complete karein. Aur guys, if you find this playlist informative aur aapko maza aa raha hai aap seekh rahe ho, please consider subscribing. Uh liking if if possible. Share kar do kisi ke sath acha hi lagega mujhe. Theek hai? So yeah, that's it. That's it for the video. Uh thanks you.
