Creating Linear Regression Model for Machine Learning in Hindi | Shiv Kumar | Skillshare

Creating Linear Regression Model for Machine Learning in Hindi

Shiv Kumar, Programmer & Data Scientist

Creating Linear Regression Model for Machine Learning in Hindi

Shiv Kumar, Programmer & Data Scientist

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11 Lessons (54m)
    • 1. Introduction

    • 2. What is Machine Learning and its types

    • 3. Difference Between ML and AI

    • 4. Advantages of Machine Learning

    • 5. Disadvantages of Machine Learning

    • 6. What is Linear Regression

    • 7. Prerequisite python numpy sklearn

    • 8. Steps in Creating Machine Learnig Model

    • 9. Introduction to Google Colab

    • 10. Implementing Linear Regression for Prediction

    • 11. Project Work Finding Regression Line and Conclusion

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About This Class

In this class , I will teach about building Linear Regression Model in Machine Learning for prediction problems. Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It's used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). 

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

And, linear regression is one of the most popular regression technique used in machine learning. 

In this class I will cover the topics:

  • Introduction
  • What is Linear Regression?
  • Python numpy sklearn
  • Steps in Creating Linear Regression Model
  • Introduction to Google Colab
  • Implementing Linear Regression for Prediction
  • Project Work Finding Regression Line and Conclusion

After finishing this class, students will be able to work on machine learning problems which requires Linear Regression model for prediction.

Who should join this class:

  • Students aspire to become Data Scientist
  • Software Engineers 
  • Data Analysts
  • Junior Data Scientists
  • Anyone aspire to learn machine learning and its models

Pre-requisite for the class:

  • Basic Understanding of Python Programming
  • Basics of Machine Learning and Data Science
  • Interested to be part of Data Science Ecosystem

Meet Your Teacher

Teacher Profile Image

Shiv Kumar

Programmer & Data Scientist


Hello, I'm Shiv Kumar. Programmer by profession and also by nature. I have worked for more than 10 years in IT and software industry. I am here to share my expertise and knowledge.

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And you start out your algorithm, Hey, I'm machine-learning, model trained care thing to algorithms. When I got the more subculture data to a different cut that or not. But he had a tautology. Not gather data or not. I'm putting up a modal, cutting out OData equity cynic. But our challenge, machine learning may or to Kosaraju algorithm or to have all the data. You see KPC scribble, process guy at the head. So respect to algorithm per process carried out east come body Jarrod, significant impact or the head recharge slow, but Joham, machine learning algorithm gets worse. You've got the hippo data equity sonic Birra problem, hey, or data quality son Keith LA, Chicago, machine learning with 19 per year per pumpkin regard. Those are Dominique AKI, interpretation of results to be a problem heck UP. Bad but are chattering, shaky is still determined can now thank you. Machine-learning algorithm key effectiveness can, um, do is exert telescope, Parkway electric is interpret cursor back. So happy. Cassie, up, interpret cortico up CARTO. Knowledge here was trilled images, will keep key data co-op analytes, cosine oh yeah, do predicts, encourages PMOS. Current knowledge. Guy was glibc scheduled machine-learning algorithm, gas interpret CTO, spray bottle, which a print cut the hippo data, your result interpretation there will be a limitation machine-learning CALEA. Machine-learning casual users have a limited overhead or so per year we bought the other austerity name or Tiki homicide to how many imagine, yeah, hey, machine learning algorithms or trained cartel, way uptake got the weight. Yeah, Joe Homais, door protesting Catherine firewall, blocks and around us I got a digital camera. They can accumulate ciliate the high-tech EEG data, machine learning algorithms or models or they have a failure, nothing. Or UK who spoke out. For us, our budget changes a, a thorough savvy data, job, omri, past data, huge new data. Sybil could be unrelated either to a machine-learning ago. Digital dynamic problem with your reserve went up or down soccer, yeah, hobby. Yeah, a machine-learning disadvantage in deep learning, major CQI, our training time doth ever bought each other. Wacky machine learning where we don't training time, have word data or data. Yada, yada. To training, hold your time. Be less eager to gather. Data is outcome cannot be hold up. I know you're happy. Data gathered, your data gets outcome can now is going to be, it can be at equilibrium limitation Embassy in London. Got it. Bowtie are notable. Limit is LMS inland inky key areas at the hip. It was kept pretty machine-learning bodies susceptible overhead, or go home look errors, pay data. The only the term Innerscope come connected courses. So Harvey, say diagnose cannot occur, it cannot contain WHO data heck, Mosaddegh cases me, Yuki. Mosaddegh cases me, Yuki was draw me modally. Adobe algorithm includes cows to underlying the complexity and maths. It was some multi complex or the heart or through other can you bought Zara impact? Does it ahead. So you take a limitation, have machine learning car or immediate t0 possibility come with the silicon immediate predict cells TO machine learning system. Qsr was bulimic historical data, so we had lunch or something. Go previous dataset was a machine-learning non-cardiac or anybody you OD, machine learning algorithm. Oh, status, data coords or the exposure malaria, licking your problem. You'd be at the Akira Iraq Asara data machine learning training may also feel who bought the accurate predictions than enough the heck you cuz causality talk about empathy. Though, you're happy we hammered the unmet acknowledge that a data for machine learning model to export and knock on equity data, Asilomar, Mr, painting or atomic split carbohydrate distinct it out that or training data to start to encourage the hair out. Which part? Yes. Or 20 percent QSR complaining authority person cannot discard the end, you stick iambic. But actually like Yamato models get an accurate head. So ha, ha, data machine learning model to export or ghetto hump, a 100 percent accuracy. The 100 legacy archaic smile or walk boss or a production environment, my body. Because then you'll have your machine-learning algorithm doesn't focus is my fail on a lucky guy or Java variability can now hone our limitation him machine learning. 6. What is Linear Regression: Take references. One is classmate or intellectual man Dan and Jay can linear regression only in 100 precent Kwame copy which contain or they can accumulate, regress unquote Cassie, Python port cut-through implement concept. The HC, DC keys k1, MF predicts and condemning, yeah, Nicki protocol dependent variable. It can take men and 20 Wolcott change on a path toward those subsequently analytical integration of attack, regression, statistical modelling method metallic, this Mia Hamm regress analysis container yet but I can Nikoli key eq dependent variable or it dependent variable, dependent or independent variable habituate catalysts and say Yannick independent variable edges Kwame log bot attack, outcome variable, Catania dependent variable can look. Outcome variable, Yeah, Nikki Joe Hamid, prompt hotel. Whenever I love everybody and final output melt I was calm. Look outcome variable or dependent variable, voltage. Dependent variable is labeled the two key independent variable, speed depend hotel, Hey, Joe, predictors Jacobi, yes, yeah, features bolt and you can just have so much to geeky up. God your company up. Suppose applique complete and Carter company may propagate your loss. Yaphet Duke who's got annual revenue on a monthly or quarterly revenue whenever it will. Sadducee GOP, depending on that rate, will depend gotta cap kidneys and ready to hand opcode profit kit. Now, Colossus get nota, apt-get expenses could write you Balsari TOP upcountry when you do help distribute, decide can never leave factors yet. Feet just say independent variables, voltaic, UK, eco-cities perpendicular the leaking, do revenue here. Well, our enthalpy independent variables just Nikki implies salary. Yeah, feed up, got profit loss, yeah, upkeep expenses, insanity, GOP, depend Carter, outcome had your revenue house kilohm load dependent variable multiple linear regression estimator has this kid through homeopathic Yankee in dependent or independent variable, may God really sensitive play or ISI liaise called linear regressing can take yuki, job come dependent or independent variables can be quite a big linear group. Tala, hotel had he listens to port, I have to say I'm logged, linear regression. Catherine ham, or is it just the capitalist state linkage to some the call got delta wij Col2 and mx plus c, you happy? Or Waikato. Do power have will came easily to clear power, you'll have one hastily say, linear regression Volta him. I've got power is quite mosaic. Cuba has, that'll give it garb, Hodeidah, do you linear equus and nucleic, straight-line new bubble kick, Garbo dagger drama, glass-like McNeil and I guess the key is updated every happy happy to exit or year. Yes. Xx is by how many x values plot or y axis, the y value. Yeah, happy exit. You have warhead independent variable X k where loopy Hamlet, wipe predict correct, right. Support key width or height, k, Sundaram, Milgram, they can't. Exhale, will wait. We've got QC Vertica or wide yoga, height or a yap it up as a techie, height or weight Godot, to really listen to their voice. Empedocles octane or like a completed profit or loss pay Hamlet, who's gotta When you decide costs octahedral, yeah, happy ego, point. Say, yea. Well hmm, it, but I can knit inside points k beat me XOR y, we're looking between a mic, linear decreasing model, banana hotel had to, yeah, happy homology or linear regression model calculator, you happy, straight-line find Canada jokey. It. Hedonic x, y politically. Or he, why'd you walk in July in Hami prompt or ECM look, regression line voltage, RDA, straight-line, Hotez Lee, I'm Lucy. Simple linear regression be bolted TO HUM, LOG, yea, yea, getting Ag, solid data points. Somebody on God is CAPM model, but I do make Nehru alluding to Hami x-value rolling it or my y corresponding y, it predict car category. Or x or y, Cajal, equus and alkalemia Nieto colorblind is Tanaka result with this at time rate. So linear degrees and Kudu equation, Hanwu, straight-line cake with some caveats I otitis, a straight-line occupy Y equal to m x plus c. So Linear Regression Chi percent v y is equal to MX plus B plus i, e plus bx. You happy? Erb, Yap, presumably I'm Joham. Okay. What constant, whatever disabled or PCM's. Yeah, happy to have our interceptor intercepts. Diego. Ya think at this point come later. Yeah, Hathi. X-axis. Ok. Are you happy to intercept kalguksu? Diego lentil tie is able to have or do you just slope? Slope, angle, AOC unlock emboldened, right? So what c or m to c and when I say E or B to have regression coefficient voltage dependent variable, y, predict cutting it on X2 Omega passe, or the predicates over E or BY looking versus pay, your BK constraint had been ham or present Voltaire, yeah, peer coefficent optic lesson bolted jam linear regression model of an angry thought linear regression model, indoor value scores, predict categories can be split, is equus and memory ALU doll k, y, y1, NewCo Asana is pretty combining. Yes, SEX tiling of assembler semi YOU element doesn't have an x, y pair been laggards get through and logic predicts some line decide construct the hand to linear degress in Kibera may get clearer. Jag opcode Javan looks and says I'm cutting it. Python, Matlab, cochlear or deck as Dambisa, carbine gay. Up, linear regression. Linear regression. Predict sun, Yeah, forecasting may usual digest that Kim and ABACABA diarchy, height k versus belt width predict costs or yeah, pin up Cassie. Does it get called Berlusconi 1D and the only way we'll Sadducee job, they listen, cut-through, podcast construct the hand. Or dependent or independent variables can be 20 lessons. It stop it connected levy. Huge caveat. Yeah. So I'm ugly. Lecture media, happy. Linear regression code Python program. You get three implement Cauchy. They can get out Hamlet, which credit card given the kinect camera prediction values at the end. 7. Prerequisite python numpy sklearn: So Hamlet richly lecture me the Cauchy, linearly crescent Carlota, kinetic lesson. Cassie, straightlining question, Coppola cacti whites called m x plus c job family, nana, keep war. Yeah, wij equal to E plus Bx, Bx plus a nap. And we are lucky to have interceptor or b coefficient array to EOB Clicquot presenter. Yeah, constant like a scary crescent, good. Doing coca Voltaire Co-op Centre, regression or ERB where lookup basis behind log x, e or Weka visit pamela white predict obtained right for years. Similarly straightlining notional diagram, we're gonna Mhm jack on newer lithium logo near hmm, low IQ linear regression model cuz you can get distributed spam looked. Pretty cutting edge. Okay? But linear regression could implement kinda polemic pushed TJ prerequisites bedrock, honesty. Was, was it valid? Your teacher will have Python programming language to Python programming language KaiC, high-level programming language head to our quantity. Or I got up to about began and he began to hit up the squeaked by Jackie. A basic understanding of the IGY bucket. A program may huge columnar warm apple, but others are longer to Python. That would be a machine-learning CALEA, UK subsidiary, popular programming languages. And enough machine learning, deep learning, artificial intelligence. By knockout the enthalpy does have, had NumPy, NumPy library, hey, yea, and demonstrate arithmetically huge chaos bacteria. Deja, by autonomy, inbuilt Aereo tile licking, NumPy, library headroom. Python may module that just comm loop item huge constructor and demonstrate at each go support Qatar or diego by Tanaka enrolled at a housekeeper Lama apologia fashionable to have border that convenient or that huge. The upper machine-learning when numpy and it got bought data use automaton NumPy. I'm logo, which can only happen. A programmer is good law ham looks like London machine learning Geico package is calm. You can scikit-learn odd East major scale on Weibull det a yam, which can engage scikit-learn coal classification problems can become huge, cut the hay. Psychic Lancome local regression problems can be huge Content API that's I can multiply it by liquid discuss together, can execute this for YUM looked pretty cut to him. The topic continuous radical break can Nicollet exists and which can give me up demand relax after him. Thesis strategic clustering problems can be unlocked. Scikit-learn package sky, which cuts the hetero classification, whole gadget, sticky, male-female, profit, lost mother, happy home, key, gioco, it, ghetto, cataclysmic, classify Cortana WE OPM contain classic resembles a classification problem. That's a key email Hogarth for email modality can be spam detection, gotta hacky consignment, spammy. Once I email, buddy, I played. Though spam email direct cannot quit classification. Spam true or false. Is Pam Haryana DO categories over a two-way classification problem where this image recognition or gagega, human cafe say yeah, and he multiphase a dog, a cat. Classic weekend, Sunday autosome looks, classification problems. Well, they're machine-learning. May SVM, to her name was order an impoverished Yes. I say algorithm edges come look, machine-learning which cut the head classification problem. Luckily, Jessica multiple linear regression, the classical era Hitler linear degradation. Calea concerned multiprocessor applications. And then secondly, never guess and MapQuest sector just to keep the stock prices cooperated connecting the navigation problem and drug response drug, drug Jessica be Colonna vaccine, Tish Peoria to Cologne, Aki vaccine. God responds. The upto a two-way data. What are good or bad? May you ECOSOC dental drug, guttural response it, we'll be homologue of the Crescent get through, predict comes to him or skeletal algorithms and more cones can say, hey, as v-out in Yiddish, Polish to nearest neighbor sailed an impoverished, we think there's some really huge gasoline, diesel fuel problem with clustering problem. Yup it automatic. Group get the similar object going to say it's just a cap is imaged with the exact same ERP. Yet clusters been eig, a look-alike plasma technology does say tomorrow they can Gillespie Joe decagon awake similar objects and wakes up cluster k clusters. But the scope clustering problem will tell you happy gaming but are popular. Algorithm hemozoin loving way. Clustering algorithm behind Lincoln came in multip Jada popular. Classically, if somebody gets on our clustering, Gangetic yoga or scikit-learn library and machine-learning. Calea, huge key aspect here, scikit-learn core diamonds, nullity, reduction, CALEA model selection can be a preprocessing, can be huge. Carry out that, right? So yes, I think Egypt, oh, I've listed lecture Madame hands on continually. Get R3 phony. Okay, so dilemma to him next six and my job next lecture, media happy I'm low hands on cutting edge linear degrees and pay. 8. Steps in Creating Machine Learnig Model: So it's like tell me, I'm goning geeky linear Dick, listen. Listen. Could implement connect limbic. Steps, follow, can show subsidy to Hamada, boss sensibility, don't IT? Hump but realistic Miami-Dade acre taco null data or data preprocess cannot already that would transform can now that I think is a porch gap capacity, high dot, ups company said illiterate, good revenue related data hit the wabe, Celtic support, the quiz points missing what they can see. The portrait you see him play curriculum, missing a Jacquard, kissy months got ever knew missing a yak which Profit and Loss connect up, missing the course data, missing whole thing about becoming larger than what I've written with that of course, went wrong. Voltaic, which were missing. Values, Guam luck, data preprocessing mate, yeah, our transformation misconfigured. Got dental school to exploratory data analysis. Still happy. I'm no good alkaline lakes cartel to V problems. So small tears come look fixed Cafe attack, India dot data_jags, Scalia, ugly processing, gliadin model may you, which can make a little happy. I'm looking at the key supports, key up capacity, hydrated data, hardcopy. Would you just get a height? Yeah. We're missing half of them get an achy Yamanaka. Who's convened, tickling your happy, you average value, Draghi, Hamm Joy, missing values, young girls, Pembroke, evidentiary logo, put carding, it'll be seen Omar dataset completely negative IQ that aka our data fixing or gay or data preparation hogan Yeah, data transformation, Noria, gay home contain data preparation or data transformation. Stigma was cable and I'm logged in the lending listen Komodo or not, they are data driven by fossil telescope. Fixed cut the hair. What's model may tacky model dyadic OJ. Each job only willing to lose weight or height k categories you hit was called Hamada models so much that a patent monopoly escape out. I'm Luke modal way to Joel. Score protocol, tangible kit life ECN tells me to skip coefficient value has disempower livable, kidnapped it was k by them look, modern drama at the airway. Taste can predict, got the hint. Nehru and watched ikea relevant l k. So you're happy, I'm lookup dished. Got the Hindi Dakota, Tama up, nobody say permissive print DAT 80%, um, look, training, 20%, texting Kolisch quartet or testing the deck is other modern goal dish cartel taking about it predicts and my looks. Yeah. The drain hole, yeah. Tom Luca cutting inadequacy pose Kwame, look, based got there. The whopping, I'm Joe, 20% didn't want to, but our dot density that was Cybil see spam look is model may wish to re-look or Flipkart extra lookup it Qatar, why we allude to Hamada predictable cathodes conducting. We'll get our ducks. I, I, though Yahoo gap would I process machine learning regularly? Some Yamasaki learning process may not go with Jackie data preprocessing the leaky modelling caissons delegate, pasting, dished split select model predicts some typecast them. Got to him along gag on new undertaking, a happy home look. Orderly can go only not aggression model, coin play mentoring. Though, yam, contain next lecture me. Explain calculate ING what I'll call putting someone magic up linear regression model. Kathie implement goes like the hat by autonomy or the happy I'm low NumPy, scikit-learn guys Killen, linear modal, huge carrying it or you're happy, I'm Luke NumPy adequately, which cutting it. So ugly. Lecture my gap em and some Carnevale. 9. Introduction to Google Colab: So it's like tell me I'm lonely Nicholas and go implement current Joham. Look proper chord licking your linear regression model, coconut cutting corners, get room looks Core present, Vergara paddock, an NGO or sat me. Hmm. Why where lookup pretty cocky. They can get their hands on system Honeywell ag or it's ma'am, load linear regression model go implement can engage or is Gideon may use Google colab. So Google collab, courageous or capillary tube provided not bad, good vitamins and Hauger duty, droopy done notebook of animism, alga, IQ, cloud-based development environment it by tonically machine learning collaborative deluge k2, where say hey Google Lee, lunch, GitHub collaborator tree, though google collab, Google Kuulab allow gotta hit Python or browser-based application, whether I'm gonna make a little Arabic, we, we hammer configuration connected closely, but again with Python called oxy download install connected with your Rodney, I'm logged just Google collapse Django and I'm look, ignored booklet getting, get Ottawa become locked direct biotin on machine learning. We are packages home logging, port collapse, I import car can block Lancaster AARP. We will collab hmmm editor, ghetto configuration, required, ubiquity becoming configure some connected yellow thing is a push-up. Go ahead Google collaborate, ERP, GPU and your head jokey machine learning course could implement connecting the coffee are important or the hair wore Hami free, accessible, tied UP May and it can check how say, machine learning programs to homogentisic temper uncollectible coffee, which some Alex attire will score. We have Coca-Cola patron costs octahedral gives you access to my freemium is already happy. Do up do we up the hill or Google colab notebook milk, they heard you Sqoop easily up Nicollet. Ksr took reference GitHub circles up the hills, cook it up. Maybe adults up there for you, happy. Derogate, vigorous and preexist for GPU on AgCl in Cops on Apple, Google colab, idea Jupiter, not book it that I, he did die happy hammock which connect the automatic I'm no Django order. Ignored booklet getting your lamp am look looks up there just to keep you happy up. Or typically getting already happy up, five plus four plus five, career are going up is quote, uncaring. Nine. They, they see you. So ERP, Python inbuilt data hierarchical, which become Nikki General. And the heap are the hair thick. Iap is seconds in a day up calculate career arc seconds in a day up print cutter to the rpm, just as quote unquote ingots are cool. The acting wicked dead seconds or there are cool. Now beta dagger, as I said, consider weak gig example the aware Google collab, gay base pay ERP data science may up, gets a huge cost of their world collab cohesive GRP. The, we have 400 giga Tegea are GLA hands on silicon as a polyhedral, be jumbled. The aware will go colab gave a plate be warm lobbying, well, it doesn't gotta get the click. Neither semi MOOC will collapse, which can name a kidney comfort level where you're happy the kidney does science, may have bootleg, Google colab. Harness the full power of popular Python libraries, bootleg, Google collaborate through upload Python libraries go, Asana is a huge cost up. They hold up my data to analyze ODE visualized cost up to him. What is meant by Turkey? Giga red item data, data can read carouse coffee, matplotlib, cutthroat visualize connector. Just like yup. Yup. It takes up doc ERP is co-author Matt Adam. I got output cathedral Diego. Orderly Kawahara is good Coulomb law. Yvonne getting atomic data visualization, Milligan thing and happy keg Iraqi. A numpy going portfolios can matplotlib code which can have visualizes some kinda huge oughta by blood or AGA PLT objects could use cutting-edge unhappy. 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The happy 19 P53 perky study, revolutionary happy. I'm getting cow gotta my school. Ricardo, or is coming from certain Coronado. Nikki app and read me Olga. To visualize an edge up and looked eagerly. Random data could generate k or P. Now P, xy plane pm login, it blocked QA. Matplotlib library call US turkey or Peter will hop amniotic face color equal to audio. Green, yellow, blue, yellow, yellow, divvy up color dimensional swapping, happy. They suck the hand. Essays, Israeli Colby up cadaverous at 10.9, getting into the queue count anatta is steady case upload is quite huge cost thing up, up, up naked opcode, hitting this Octavia Joby. Now I'm gonna tell you how they, SAM, look. I've got quartet can knock out 200 corporate click OK, accord that up. We'll skip Bosco to comment the natatio cuticle, somebody Nikoli, a key year data science. So you can drop quite happy, come into be Abdullah cocktail, ignored book eeta delta display, text on board except leaking out the hands to Yahoo. Google collapse like in production, Mhm login enhance onsets and predominate. Or finally, hmmm log-linear exists in Monaco implement cutting it ugly like January. 10. Implementing Linear Regression for Prediction: So pictured electromagnetic actually Google collapse or HAM lake simple program bead on Garcia Decca visualizer. Can they got Casimir? Is lecture mayhem luke, Final Project, can Joe, hey, x basis B x where looky VC's primary, why you expect where local predictor by law to subsequent religious coupled with the heck, yeah, me. Do we have a packages to leverage a warm import can now got this Gilliam import culling and NumPy, Duke am look multidimensional and is huge. Can you I'll hit on numpy Hemlock import. Numpy import clinically important space numpy as np. So you happy numpy as np huge cutting up MNI coordinate m, np dot carrying it or NumPy package 2py functions on Guam Lu easily which can do sludge of classical linear regression class to hm, link scikit library, say Scikit Learn, yeah, SKLearn library, cylinder to linear model package. I was so happy I'm learning linear mechanism Coby import can linger. So paella step yogic EM image will be liabilities say yeah, classes three, warm look, import culling, it'll Hammarby bus, ready wrangler, huge clinically, do sludge or step in data preparation or transformations. So yeah, has Yamaha mean data preparation or data exploration cup parts through the Hopis tip domain data preparation and transformation can engage me HMO counter thing to deal with missing values Othniel, whose corporeal connected courses, Catania, feet, missing rows and columns go RTD, They have yak, which mean we gotta nickel Cambridge read-only scope of Phil Carnegie courses, cut the hay or data go. A Top Transform, cupcake, rope, military attack, EMOC, huge costs like there could be null value. Yeah. Anyway I lose novel. So yeah, happy, I'm look categorizing app am look would update, have an RFP, happy my X or Y DO teacher longer. Yup. M cackling x k basis pay y value at x k corresponding y-values. So yeah, hmm, system repeat carrying on linear regression model. Go home, look trained carrying a, a modal banana. Does ketosis fit? Who named x-value Ham looked system, if it calling it will have a corresponding y value. They are great. So you're happy I'm Luke is data Cambyses pyramidal system, both will continue to happen, may crack along x. It got added but not all number yet it np dot array my huge gone well known by everyone unequally immerse. There are five pre-print, 25-35, 4545 upwards with a secure PRP, 25-50, 5K, 354515 Kurdish 25-30 J up, viscoelasticity, five-carbon DJ, screw up, 75 per cycle. Some jockey for garden is crop. 1982, Israel lookup, which we lesser kinetic. To support the Hamada XOR y, yeah. Yeah, permitted dot recept, Yahweh dot recept to NumPy, array me up column or row score defined candidates. The outcome will take gamma Hami column TA, or roads, neighborhood advocate indicator gap and minus one to one no-man's land area was called multi demonstrate anatomy, convert cut. They got Odyssey. Tell us, I'm Luke, why an API? We're not a multidimensional array and PID bananas, Hey, one-dimensional area. So you have a dihedral. Exhaled were FCRA, IQ column or multiple roads KSR data. Although IQ, Rwanda right across multiple columns add together. Though cookie has_many x co convert cars, the eye color Mona Cato, you keep all of my vacuum allergists war room earnings come here to get album cackling at the sludge tip you have warhead linear regression model made a taco fleet carrying it, right? So hmm, look by lin linear regression model. 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Hm, Look, it's modelled Muhammad camel, Decca coefficient values, manga constant coefficient, dual linear regression, K COPC and say Jacobi sin off regress on a. We have over Y equal to m x plus c, a m or C value jackhammer Mileva, hey, I'm cackling R-squared module.js score XY carrying it or has a coefficient of determination or intercept values mean Django model r squared r underscores quahog corpus in the data or intercept. Kelly, I'm Lo model dot intercept underscore cunning little yea Homais intercept key-value their data or slope, go hmmm, lung model dot coefficent cutting a model coefficient or yummy slope key value that they got. Oh yummy ham look, cackling it. Qin economic model, benign, oldest Mamluk cackling and linear regression one-syllable called cunning Alfred dot X comma Y dot VC. Abby happy, I'm look, I kind of get why cul-de-sac gotta just make column over on multiple rows on autos Kiva. You happy. Interceptors. Slope cup gave me a copy. Santos Couperin killing u2 is quote unquote ridiculous. Coming out, put camel into the opposite click, coefficient of determination it Nia into separate Nia slope it Nia on now wallah intercept, yeah. Yeah. Or slope you have it Nia lab work same evaluate UK, I'm Nick, which be changed to make Yahweh stick. Why qualm really SIP Gatto, CME where leukemia, pinwheel rehab, up, gambler cutting it. I won't look at linear regression. Models apply Kirchhoff's prediction, cutting it. So yeah, Pam can find canola, why predicts and cookie x k basis primar y co predict canal Yap Democrat cutting it. White predicts an equal to Y predicate model.predict yaks, so yaks, happy I'm predicts bunks on coding gain. Dorky Hammarby model can predict Krieger to model.predict Katherine Maxwell who colleague our predict function odd k values predicts like escape, I suspect, do hominid linear regression can model now skip kit-based pay. Or you happy I'm look at getting it predicted response, Yankee white bread or coefficient, intercept or coefficent going happy. Print, JOB Hami, intercept, myopic will there, which list step memory they got Kasim Nicholson and your coefficient was calm Luca wreck wij equal to m x plus c may fit Ck are there. So you have him look at em to him model intercept will directly OPM model dot intercept, correct? Plus model dot coefficient into eax anarchy, yogas T, model dot intercept year Mary Jo value where Yahoo AC or Yahoo get m coefficient or 3x 1202 0s value Kwame law, It's simple, straight line equation. We are regression line Kate Freed, correct. Wipe Brit Co, phonically at the white bread Cal gum modelling intercept plus modal coefficient into eax. So yummy white, equation D, They got y-value giga, headache, model, intercept key, Kelly or Yup, I'm look predicted response code from blowing up a print, cutting it. So IBM lobby score and killing to the Tamika or put multiple Yap in the KML output MLA predictor response 102421152730 or is made they can gauge or from an aqueous and we do Camara, your model predict caribou, Kalevala, OR DO hamlet ERP intercept requests and mu value fluid Kirchner, Joe, Nikola, Warby, Haumea. Same result there and no-no here result will call VCE, will police embryonic yup do model predict Korea or Joe coefficient k basis B, I'm low, manually fried turkey and the Columbia Homais CME y-values. Then you want to hand to Yamada model, the arrow guy or A-bomb. Yummy. Predicts and be the intellect album cackling, yum, update model.predict cunning and new datacenters and you're happier, thalami obstinate, but on a day-to-day basis per He predict QA, right? Hmmm cackling, it can't make NAD dataset linger or school Hamlet is modal. Middle-income. Maxwell lose our y-value. I'm look predicating the app Hamlet calculate caret dx mu dx nu gamma of nu x by loot, you haven't Mexico np dot orange ten, or reciprocal minus one, comma one and Q one column or multiple rosemary 10 to nine and a manana, n and p dot ten cachaca, 0 to 900. They got like a Tokyo digital scuffled, 100 others though. Okay. So you calculate again predator and 0 to 9100 to ten, you make anything that you do. Two key, du by dt anosmia indexing should rotate Eurocentric 0 to nine giga and get those ten elements cake. And even IGA, I'll just come locally, say minus one comma one current McCloskey, one column of our rows, get maybe data points on growth narrows been other economies could print corrects. New Nucor, dagger, d rho two Niantic and Emile Galle, right? A-bomb cackling gave predict centering model could huge Turkey. So you hop in Mecca, which control? The optima. You'd cut out, Jaap him. But that can now, why new? Now I'll skip this. It's been a while. A y-value Hammarby model called huge curcuit Guan millenia regress on Turkey. But I'll just go Hamlet train Kayak is payoff Nippur anyway, extra y, value, spare, Joham, 10X, feed key attack, Happy Valley step may, right? Yap it. Hamlet by Alice step Mays quote Joe, data feed kettles kept him a modern, we'll get a stamp query x where Laura linear term and a y values. We'll Lang Happy model. Why new kilogram model.predict xx news. So model.predict constantly Jovian Maxwellian rolling Awami, IQ was kick corresponding y value they got do we have an immoral go train key wire. So you happy Hamid. Why? New eligible jangled otolithic Tamika, new eligible test. The camp MAY well Ujamaa level camera it it 0.9499.5 to assay. I may thus x-value CALEA. So eastern US, they humble yet to have Wacom aria, Scalia, ne y-values, Abdulhamid Amara, linear decrease and more electronic Arcadia to East allocates the HUM LOG linear regression model called huge construct. The hymn predicts an canonically though linear regression AARP, I'm low linear regression modelling. But a massacre Tokyo opcode years. Some admire her work in linear regression co-op Cassie, huge cost something to subset by lamb import cutting-edge Ruby libraries, MHA classes, two-year, pyramid recreate corrects or why feed Hmm, add ACO, model modality, linear regression, classical call Turkey modellers fit my x and y culprit career progress and come model humble, not a half-filled whose model cut-through, I'm Luke, 4% copper that Korea or y-value could predict, correct? Or you have family coefficient, could alka wij called them extremists plus seemed to be a miss him, you lose millenials. Guadalajara model data with some leak. Data. Create K x nu, get o to nine I carry when I go home there is model dot predict amygdala or nay, why you Andrews will get reached that occur hmm, linearly the so-called huge costs, OK, the hair predicts and CALEA. 11. 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