Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science

[@kiranupreti5619]

i really can't describe how many platforms i went through like statquest, datacamp, geeks for geeks, stanford, udemy ,towards data science and much more but never saw such explaination . this is the first time when i am commenting on a youtube video .your work made me do so .you are seriously a great teacher krish . thanku for sharing your knowledge in such a clearer way.

[@mickolesmana5899]

Yep coming from StatQuest.....

[@RhythmShakti]

Me too

[@dharamveer4899]

I think it is even better than 3blue1brown's explanation. Don't you think so?

[@AkshayRakate]

try applied Ai course

[@Nice_lolat]

Other sites just discus about the code. I believe a ML engineer should have a good knowledge of some aspect of maths and statistics

[@RajaSekharGowda]

Sir, you are my role model... Your aim " sharing knowledge " is incredible... #Keep going

[@dhrubhajong8133]

your channel has brought a huge transition in our lives , especially people who are looking for a career transition. Thank you and your channel for sharing your knowledge and making it easy for us .

[@ganeshrao405]

You're one of the reason for me to dive in data science community. I learned from your videos and interesting , felt more to learn. Thank you Krish.

[@pankajsoni3215]

what's your position now ? please reply I am a freshie to data science from nit

[@NGH8731]

I must say Sir you are genius. What an explanation of logistic regression. It's awesome and so simple that anybody can understand. If you watch this video twice you will never face any problem in future to explain logistic regression.

[@kirandeepmarala5541]

Saying Thanks is not enough to KRISH Sir...Daily, Your Video make's my Day fullfillment.....Praying God to have grace over you all the time...

[@shivprasadkale4809]

I did Data science program with simplilearn and also reffers much yt channels bt ur the guru we need. much simple language and clearer concept thnx

[@rashmidhawan4783]

Your explanation is beyond excellence... After a lots of traveling finally I find a outstanding platform to learn data science.. I am pursuing Ph.D in machine learning.. And this tutorials make my PhD interesting...thanks a lot...

[@sandipansarkar9211]

Superb explanation.Earlier i was always confused about logistic regression and linear regression as I was unable to understand the mathematical intuition behind it.This is mainly asked as interview questions in product based companies I guess. Thanks Krish.

[@siddharudtevaramani1055]

Having watched many videos related to difference b/n LinR and LogR, nobody explained in this way. I think this is what makes stand out. Just fabulous!

[@sivamutyala3611]

Superb explanation on why we should go for Logistic Regression. I don't think we can find this kind of clear explanantion in most of the channels. Thank you for presenting us the concept in easy and beatiful manner.

[@shwetharao3894]

Currently pursuing my MS in Data Science and Business analytics, this is the most simple and clear explanation on Logistic regression!! Good work Krish:)

[@mvk11574]

this is much easier and simpler to understand than what was taught in paid course ..thankyou so very much .You made life simpler for non ML background guys like me .

[@yashodhansatellite1]

Amazing Krish.. thanks for such effort to explain it so easily. You are our Andrew ng

[@lvilligosalvs2708]

This is the best explanation I've seen about this topic. Thank you very much, Sir.

[@flse8337]

This is what happens when you really want to teach and its not all about the money. Krish took the time not just to teach the concept but also connect it to reality. Good stuff boss. Hope the quality of your work only increases.

[@neelamegammuthuraj8982]

You are amazing ... was having a long run doubt of why to use logistic regression ... fantastic explanation. Great Work .. Much Appreciated.

[@vitor613]

one of the few videos that I've watched in 1.25x and not 2x. On point

[@rishavpaudel7591]

Sir I love when you say the word-------> "UNDERSTAND" :p :p

[@filipmilolipinski5299]

I love these videos. You make this puzzling stuff seem so accessible.

[@equiwave80]

Simple concept but the explanation you gave was quite powerful so that we will remember the core concept... Thanks Krish 👍

[@milansood8240]

Wow sir, abhi pura upgrad ka module padha but know chahiye tha logistic ab samagh aya 🤩🤩🤩

[@tanzimmahfuz3618]

Sir you are the best teacher from youtube May Almighty bless you

[@PREMKUMARBORUGADDA]

tutorial 34 was clarified my doubts, I am waiting for part 2 & 3 of performance metrics for Classification problem in ML. Please upload the part 2 & 3 of tutorial 34 sir.

[@motunrayoakinsete]

You are a really great teacher. Thank you for sharing this.

[@Hitesh-Salgotra]

bro this comment has just came straight from my heart even the andrew ng have not explained with that much clarity even though i know you somewhere learn but your explaination is quite well as i have done ML from 2,3 online courses but i will check whole your playlist again only for the deep understanding of math behind it

[@soham4741]

andrew explained it really well , but he uses more complex language which is harder for us to relate to. I understood andrew really well too after watching his logistic regression videos twice each.

[@moonlight-td8ed]

bro every youtuber learns from andrew ng

[@anandhuded3647]

Best ever explanation thanku so much for your effort👏👏👏

[@tusharjeena7081]

you are great sir ,your lecture help me lot to clear the concept in ML ..

[@ManishKumar-qs1fm]

Awesome explanation Sir, m waiting ur video on daily basis, sir plz upload maths behind every algorithm, which is helpful for us

[@amitdebnath2207]

Brother take my gratitude. Huge respect for you.

[@9902152322]

explanied very clearly and in short, keeping going. all the best sir.

[@shivarajnavalba5042]

Superb content & direct to the point of discussion without wasting much time. Thanks krish,

[@gitadanesh7496]

Hi Krish. Thank you very much for your videos. I just have questions regarding the reasons you mentioned why we cannot use linear regression for classification. 1) outliers : in general outliers make the model inaccurate and we should fix or remove them. 2) what if y>1 or y 1 and and y

[@saptarshibandopadhyay5902]

I was searching the comment section for answers to this question, but no one even questioned this

[@Saikumar-mx7co]

It is actually lies between 0 and 1 for most of times or may be refers to error while implementation

[@ROHITKUMAR-gq8bz]

its really wow!! thank you so much sir for this beautiful explanation.

[@deepakpanigrahi9601]

Krish bro you are the best and your contents are magic over the internet!!

[@SAN-te3rp]

Can anyone tell where he stays I have to touch his feet for this incredible talent of teaching maths 🙏🙏🙏🙏 and students who are from primary schools ,don't watch this video and dislike that just bcoz you didn't understand

[@kiranchowdary8100]

Thank you bro please continue it helps a alot

[@dm3248]

Lectures of krish & Andrew ng course on ml is a nice combo..!! 😁

[@tiverekarrahul]

Thanks for sharing this Very Very Very useful knowledge.

[@shaikmahammadali8043]

Mind blowing explanation guruji . Happy teachers day

[@ayanmitra3997]

Awesome explanation sir. Mind blowing

[@teetanrobotics5363]

one of the best tutorials.I just love it

[@vineethm6930]

Excellent lecture sir, i am going to watch the entire playlist. Keep up your good work. All the very best sir.

[@vdmaheswari4943]

Great lecture, thankyou so much for everything

[@johnmarcampbell]

Very nice explanation! Definitely helped my intuition.

[@ganeshreddymangunuru5001]

Awesome sir👌... superb explanation 🙏... please continue to upload videos sir.

[@Raja-tt4ll]

Very useful video. Thanks Krish.

[@shaunakchadha4204]

Krish sir, you are the best....

[@RhythmShakti]

Your explanation is superb

[@batista1228mohd]

simple and effective explaining .

[@vinodkumarreddy7696]

Thank u so much sir... Very good info 👍👍

[@mayankmotiyani9144]

finally i understood the concept behind it :) thank you soo much sir

[@vidyasagar7860]

Thanks sir , again it is very simple to learn everyone

[@siddharthapothukuchi2687]

Best explanation, better than my prof😂

[@bolthirani9504]

listen between 3:05 to 3:15 with deep learning (deep hearing) there is fun

[@amarsinhjadhav8156]

great teacher ever❤

[@sardorabdirayimov]

Thanks for sharing your another great tutorial

[@mansoorbaig9232]

Comprehensive explanation, Part 2 and 3 please...

[@krishnaik06]

Check the complete ML playlist

[@abbaskhan9063]

Thank you for such good explanation.

[@abhimanusharma1645]

Easy yet effective.

[@PuneetSalwey]

Brilliantly explained

[@shabareesharyan]

You are an amazing person. I hope you know that.

[@marilanavandeventer2844]

Very good explanation thank you so much

[@Jan_Jan_]

Very clear explanation! Looking forward to more videos!

[@MehakKhosla93me]

Sir your lecture series has made my learning simpler. Thank you. If possible, please build a playlist of different neural networks.Best Regards

[@123XTSK]

Why use logistic instead of linear for binary classification ?-The reason has been well explained.Thanks

[@pranjalgupta9427]

Great explanation

[@swethakulkarni3563]

Best explanation so far!

[@sunilpatil1923]

Very good vedeo, well explained...

[@roshankumargupta3711]

Great video. Can you also help me with the difference between Multiclass and Multilabel classification?

[@wealth_developer_researcher]

Best lecture ever

[@varunmajithia2470]

Very Well Explained.

[@akukweanselem2694]

Great stuff keep it up 🤗🤗🤗

[@venkivtz9961]

I think the main problem is we use probability to find whether it is obese or not, when you use the linear regression, you got probability less than zero and greater than 1, which is not possible, hence we can't use linear regression in the case of these type of classification problems. If i am wrong, please correct me. I appreciate your explanations on your videos

[@shivadumnawar7741]

intro music is good. great content.

[@natasham1811]

Great lecture

[@jupinsgil]

YOU ARE THE BEST

[@arindamghosh3787]

Thanks a lot for your amazing videos 💜

[@shrikantlandage7305]

This person is Gem

[@punitkatiyar8564]

Hi Krish, I loved the way you are teaching us about data science concept. I saw the above vedio and have one doubt in it i.e. If we remove the outliers from data, can we use linear regression for classification problem?

[@rajatchuttani1218]

Great job

[@shadiyapp5552]

Thank you sir ♥️

[@Tracks777]

amazing stuff

[@manishkatiyar2403]

Thanks

[@harivgl]

What is the problem with the weight intercept on the linear regression line being greater than 1 or less than 0 ? We're classifying only based on one criteria of the intercept being greater than or less than a particular value like 0.5, right?

[@ashas7864]

well explained ..

[@soumitasankarkarmakar2591]

Thank you so much sir

[@sunnysavita9071]

incredible effort

[@snehalvaidya5843]

Thank you 😊

[@louerleseigneur4532]

Thanks Krish

[@Arkyvz404]

Thank you soo much

[@archanadevi7081]

Hie. Your videos are very informative and easy to follow. Could you please prepare a video for LR for text classification? Couldn't find it in ur playlist as well as couldn't find any other video in KZbin useful for this topic.

[@allenalex4861]

great explanation.

[@HelloWorld40408]

Thank you

[@creatorsayanb]

Great video. The voice was not recorded properly in this video. Other than that all is well.

[@PetersonCharlesMONSTAH]

Hey bro, can you help me out with from sklearn.linear_model import LinearRegression? I get an error message from using. This is the error message I'm getting. from R and python.ModuleNotFoundError Traceback (most recent call last) in () ----> 1 from sklearn.Linear_model import LinearRegression 2 regressor = LinearRegression() 3 regressor.fit(X_train, y_train) ModuleNotFoundError: No module named 'sklearn.Linear_model' Thanks bro.

[@DS_AIML]

Use from sklearn.linear_model import LinearRegression .You are using Capital Letter in linear_model syntax.

[@niharikamiryalavk]

Just wow!!

[@codewithedison2199]

very good

[@kirubababu7127]

I have a doubt, We are not passing a raw data to train a model, Anyhow we do preprocessing, from That we can remove outliers.

[@nitinverma8585]

I doubt your reasoning... Shouldn't we remove outliers first before fitting any model? If we have outliers even in regression problems, won't the results be bad even for linear regression because the best fit line will be wrong?