Sir....tear in my eyes 🥺🥺 I thought problem in my side that I am dumb...But what you taught all are like diamond 💎 No one can take so much time even 50000 ₹ course to make concept clear.... But now I am sure the problem was not from my side or more pricisely, best teacher do not need bright student to teach they can make the dumb student bright😄🤗
There are no bad Students , only bad teachers. ( quote from Karate Kid) you were involved with wrong teachers, but now you have finally found the best teacher.
Best explanation of pipelines than any channel I have gone through today. I am about to appear for my interview soon and had no idea about pipelines honestly but I always had these questions in my head about how all these individual steps will take place when I deploy my model on cloud, Now I know how it should work rather. Thanks.
Thank You very much Sir, You could have taught the same thing in 10 minutes but you took efforts to saw us consicounses of not using pipes so that we can understand the importance of the same. Hats off to your work. Thank youuuuuuuuuuuuuuuuuuuuuu.
Thank you Nitish Singh. Your teaching style is awesome but the thing that impresses me the most is how easily you present the complex looking concepts. Your 2+ years of long journey surely paid off.
Very clear explanation. This is the first video on youtube in which each and every concept related to pipeline is explained very well. Great job sir, keep growing.
@@tusharkhatri5795 Because if we use fit on test set then the model will also learn the test set values and then there is very high risk of overfitting.
Making ML much much easier for every student out there....really you are an awesome teacher explained every procedure and techniques so clearly.... thankyou so much sir❤❤ best videos
Really thank you so much sir, itne easy tarike se smzhane ke liye. you know, mere ek friend ne kaha tumahre pas "Ineuron" ka subscription hai phir bhi tum campusx ke videos q dekhte ho? you know what i said "great content with easiness" . that why mai campus_x ke videos dekhta hu.
Hats off to you sir, the efforts and honesty you put into making any video to make everyone understand the concept in depth, are remarkable. This is what we call hard-earned success with full of honesty and dedication. This channel should reach a great height and will soon become one of the best channels for data science courses. Thanks and keep up the great work.🙌
woww 💥❤🔥what an awsome vedio i am following all the vedios from few days , i got so thrilled while using this vedio , because i get to use all the things that i learnt from previous vedios , and what a well organized and well concept reprasented vedio . thankyou so much , i am doing masters in data science and AI in University of Liverpool , and even my lecturers cant teach this good .keep up the good work sir .
Thankyou so much sir..i gained a lot...like massively a lot!!❤️❤️❤️... teacher's like you🙏🏻❤️❤️ gives us the motivation to study and explore more and more concepts ❤️
Amazing video as well as a series. But just wanted to know as u said developer needs to write the code in production and change the code of the file **predict_without_pipeline file. But once we export our model we can handle this in our API's routes it is that hectic ?? validations laga diye to ho jaega just 1 hi bar to likhna hai code. But yes pipe mechanism is very clean and understandable
Best videoes of the TH-cam..ur the best who gave the lots of effort without talking money..ty so much for giving such most valuable time for creating such a amazing video..it will clear all doubt regarding data Science....such a amazing playlist....🙏
Hi Nitish sir! Hope you are doing well Nitish sir. First of all, Thank you so much for all the knowledge that you are sharing with us free on TH-cam. Sir, I am a college student and tomorrow is my AI & ML engineer interview at a big tech company. I want to revise all the stuff that I learned for your TH-cam channel playlist 100 Days of Machine Learning. Can you please share with me your Microsoft One Note file which you used in your playlist? So I can revise all the concepts of ML in a few days for my first AI & ML interview. Again Thank you so much for all of your efforts which you give us on TH-cam without any fee. Kind Regards Aqil Saboor
There is an slight issue in the Implementation logic . Since in above pipeline output of trf1 (imputation column transformer ) will be input to trf2(one hot encoding column transformer) . Since we have imputed the age[2] and embarked[6] column first and then PASSEDTHROUGH the remaining columns . Then the output of this trf1 columns will be in the order of (columns that we have imputed and then remaining columns) i.e :(age,embarked,pclass,sex,.......and other remaining columns) .Here the index of the columns has changed from the original dataframe (df1) . In the next step result from trf1 will be passed to trf2 . And we wanted to mention column sex and embarked (index 1 and 6 respectively in the original data frame but now the index of both have been changed) , which is a mistake . So if we want to specify the correct index of column sex and embarked in trf2 it will be 1 and 3 respectively .
Sir, I have a question. When you are preprocessing or modifying something in this x_train and x_test, what happens if you drop some values in these two? Will this affect the y_traina and y_test?
Hi Nitish How to deal with cases when we have to write custom functions to deal with certain type of preprocessing? will that be taken care by pipeline class?
please note that once transformation is performed using ColumnTransformer then that column shifts to the beginning and thus index of every column shifts. So for further transformation just keep this in mind while assigning index to columntransformer
Doubt : The purpose of cross validation is to train the model on "whole dataset" in "folds", then why are we passing X_train (Already splitted dataset ) for cross_val_score(). Shouldn't it be X,y instead of X_train and Y_train. Sir, please explain. Also let me know if my thinking is wrong.
@@campusx-official How will you pass the entire dataset? The entire dataset will have missing values. And imputing missing values before the train test split leads to a data leakage problem.
This is my take from cross_val_score () ...when you do random test_train_split() for a dataset the algorithm would give accuracy of of model the random data which it splits as training data and test data ..but how can you say the data which was the part of the training dataset was best to train the model?... there is no way to say to model pick rows 10 to 100 for training as its best and 1 to 10 for test ..hence this things we use it cross_val_score() and we would definitely give cross_val_score() the entire independent column and dependent column data no splitting of train and test is done for this..
@@aination7302 We will pass entire X and y while doing the cross validation. And while doing that we pass pipe ( our pipeline consists of imputation,one,scaling,feature selection , model) , X ,Y,cv=5,scoring) ....so in this step our X and Y columns in dataset passes through the pipeline and so X and Y get through all the process . So ultimately no, matter of data leakage may come as because One of the best ways to get rid of data leakage is to perform k-fold cross validation where the overall data is divided into k parts. After dividing into k parts, we use each part as the cross-validation data and the remaining as training data. Better to use k fold cross validation or Stratifiedkfold.
Nitish Sir in this video, while using transformers for the same test input we got result as 1 in pipeline and 0 in normal way. two diff outputs, iam not knowing which one to interpret as correct. Hope you answer this.
I have a big one question: What is the difference of build a Machine Learning application with Pipeline and to build a machine learning application with a OOP technique? I see that it is the same.
Pipeline ko agar khud se explore karna hai toh ek baar model ko streamlit ya phir flask me dalo phir tumhe pata chalega ki bina pipeline ko use kiye kitna code likhna padta hai aur agar pipeline use karte ho toh easily ho jata hai😊
I think this channel is better than Krish Naik's channel!
both are amazing,please don't make any comparisons,
yes bro,even i think this is more in depth and very clear
Indeed.....I have purchased tech neuron and definitely verify that
I also purchased tech neuron but it’s of no use…he’s better then kris naik…
Yes, Nitish isn't in hurry unlike krish😅
I gained more knowledge from this video than I have from an entire semester worth of classes in my university.
Keep up the good work Sir!!
Sir....tear in my eyes 🥺🥺 I thought problem in my side that I am dumb...But what you taught all are like diamond 💎
No one can take so much time even 50000 ₹ course to make concept clear....
But now I am sure the problem was not from my side or more pricisely, best teacher do not need bright student to teach they can make the dumb student bright😄🤗
There are no bad Students , only bad teachers. ( quote from Karate Kid)
you were involved with wrong teachers, but now you have finally found the best teacher.
@@tathagatasharma totally agree.. 👍
you wrote a good poem ." you should be a poet not data scientist "....... " Bad teachers with you. you should prey them by calling all mighty " ,
Best explanation of pipelines than any channel I have gone through today. I am about to appear for my interview soon and had no idea about pipelines honestly but I always had these questions in my head about how all these individual steps will take place when I deploy my model on cloud, Now I know how it should work rather. Thanks.
Very well structured videos you've made Sir. God Bless Teachers like you who are helping students learn better.
Thank You very much Sir, You could have taught the same thing in 10 minutes but you took efforts to saw us consicounses of not using pipes so that we can understand the importance of the same. Hats off to your work. Thank youuuuuuuuuuuuuuuuuuuuuu.
you are making my journey a bit easier evryday .Thank you so much sir
Thank you Nitish Singh. Your teaching style is awesome but the thing that impresses me the most is how easily you present the complex looking concepts. Your 2+ years of long journey surely paid off.
Man , your style of delivering the content is awesome !
what a beautiful way to explain such a complicated topic...hats off to your efforts Sir. Love allots💗💗💗
Exactly
Excellent code understanding and best teaching ever on TH-cam✨🔥
Well explained. Nowhere I found such a great detailed explanation like this. Thank You so much.
Very clear explanation. This is the first video on youtube in which each and every concept related to pipeline is explained very well.
Great job sir, keep growing.
can you please explain why he used fit transform at train_age while transform only at test at 8:22
@@tusharkhatri5795 Because if we use fit on test set then the model will also learn the test set values and then there is very high risk of overfitting.
I was lost like hell and here comes another great video with up to the core explanation.
I really like your explanation, Please keep uploading more videos.
thank youuuuuuu! doing a bootcamp abroad (paid 8Lakh) .. understood nothing! this video is giving me more clarity than the bootcamp ever could!!
Making ML much much easier for every student out there....really you are an awesome teacher explained every procedure and techniques so clearly.... thankyou so much sir❤❤ best videos
sir I have never enjoyed learning ML before...You're actually the best!!
Teaching style is so good. I have learnt so much by you. Hats off to ur effort sir.
one of the best data science channel
Really thank you so much sir, itne easy tarike se smzhane ke liye.
you know, mere ek friend ne kaha tumahre pas "Ineuron" ka subscription hai phir bhi tum campusx ke videos q dekhte ho?
you know what i said
"great content with easiness" .
that why mai campus_x ke videos dekhta hu.
Hats off to you sir, the efforts and honesty you put into making any video to make everyone understand the concept in depth, are remarkable. This is what we call hard-earned success with full of honesty and dedication. This channel should reach a great height and will soon become one of the best channels for data science courses. Thanks and keep up the great work.🙌
This video is literally gold brother for fresher Data scientists
woww 💥❤🔥what an awsome vedio i am following all the vedios from few days , i got so thrilled while using this vedio , because i get to use all the things that i learnt from previous vedios , and what a well organized and well concept reprasented vedio . thankyou so much , i am doing masters in data science and AI in University of Liverpool , and even my lecturers cant teach this good .keep up the good work sir .
thanks for giving such good content. I don't think anyone is teaching in the same way from base
Ur incredible.... the way you teach..... simply amazing.... thank you for your efforts... giving you in free cost...
I love the way you teach my brother.
Love you from Bangladesh.
Thank you so much for the playlist
Excellent Sirji,Hats off to you.
Best Explained i learned alot ,You are the best Teacher ❤
God bless you
Sir, these videos are really helping me to understand ML concepts. Thank you so much, sir.
Thanks a lot for this playlist. You have literally explained it so well and especially from the real project perspective. Thanks.
You are doing great work, just a suggestion change website address in description to recent one. Thanks for the video.
Thankyou so much sir..i gained a lot...like massively a lot!!❤️❤️❤️... teacher's like you🙏🏻❤️❤️ gives us the motivation to study and explore more and more concepts ❤️
bro, that was superb.
I loved the way you teach,... that granularity, hats off.
hatsoff sir! You are god at making concepts clear
thanks sir so much
you don't know
how much you are important your lectures for me
Thank a lot for your efforts, one of the best video I was looking for from long time.
This channel is awesome no one can bit of this man
Amazing video as well as a series. But just wanted to know as u said developer needs to write the code in production and change the code of the file **predict_without_pipeline file. But once we export our model we can handle this in our API's routes
it is that hectic ?? validations laga diye to ho jaega just 1 hi bar to likhna hai code.
But yes pipe mechanism is very clean and understandable
I don't know how are u, but thanks brother I loved your videos.... you make not only day every day to me
awesome. No doubt best way of teaching. Complete road map got covered.
Nitish Sir, No one is better than you in teaching ML topics.
Brilliant brilliant brilliant 👏👏👏👏
Best videoes of the TH-cam..ur the best who gave the lots of effort without talking money..ty so much for giving such most valuable time for creating such a amazing video..it will clear all doubt regarding data Science....such a amazing playlist....🙏
You are an awesome teacher!
very Nice respected sir .kash aesay teachers hamari univaresties main bhi hotay.love from pakistan
We are blessed to have ...teacher like, Nitish sir!!
Your videos are excellent . Very easy to understand. Do you have a video on deployment of model to AWS EC2 ?
th-cam.com/video/_rwNTY5Mn40/w-d-xo.html
So beautiful explain all the things sir... salute to 🎯🤗👍
Very informative, I will work on my next proeject with pipeline.
Hi Nitish sir!
Hope you are doing well Nitish sir. First of all, Thank you so much for all the knowledge that you are sharing with us free on TH-cam. Sir, I am a college student and tomorrow is my AI & ML engineer interview at a big tech company. I want to revise all the stuff that I learned for your TH-cam channel playlist 100 Days of Machine Learning. Can you please share with me your Microsoft One Note file which you used in your playlist? So I can revise all the concepts of ML in a few days for my first AI & ML interview. Again Thank you so much for all of your efforts which you give us on TH-cam without any fee.
Kind Regards
Aqil Saboor
whats the update of your interview ?
There is an slight issue in the Implementation logic . Since in above pipeline output of trf1 (imputation column transformer ) will be input to trf2(one hot encoding column transformer) . Since we have imputed the age[2] and embarked[6] column first and then PASSEDTHROUGH the remaining columns . Then the output of this trf1 columns will be in the order of (columns that we have imputed and then remaining columns) i.e :(age,embarked,pclass,sex,.......and other remaining columns) .Here the index of the columns has changed from the original dataframe (df1) . In the next step result from trf1 will be passed to trf2 . And we wanted to mention column sex and embarked (index 1 and 6 respectively in the original data frame but now the index of both have been changed) , which is a mistake . So if we want to specify the correct index of column sex and embarked in trf2 it will be 1 and 3 respectively .
amazing video sirji!!
Its almost took Two days for me to study , understand the concept, code & implementation .. Weird but interesting ..
You are really a gem sir
thank you sir, you are the best teacture for fresher to learn data science. Please uploading as many as more videos regarding ML and DL.
Best Channel to learn data science.... :)
Thank you sir for this amazing lecture
You are a legend buddy. Brilliant & thanks a billion.🙏🙏🙏🙏🙏
i had literally hand-encoded all the features in flask app cause im a beginner hahaha. i feel so great learning this now. thanks a lot sir
God level teaching!
Wow man... I am running short of words.
thank you for giving such kind of knowledge, your awesome sir❤
Sir, I have a question. When you are preprocessing or modifying something in this x_train and x_test, what happens if you drop some values in these two? Will this affect the y_traina and y_test?
thank you sir
We need more teachers like you
awesome ! thanks for going in to so much detail so that you can make it easy for us
You are Amazing Sir
So Fantastic so amazing just like wooooow!!!!!!!!!!!
really appreciate your work..Just started learning with you.
pretty straight and neat explanation
Amazing way of teaching!!
Thank You very much.😍
Hi Nitish
How to deal with cases when we have to write custom functions to deal with certain type of preprocessing? will that be taken care by pipeline class?
Well explained sir...thank you
sir original me hi changes krne thee n embarked ki missing values fill krdi hoti train data k or ussi data pe encode kiye hote
Excellent Explanations 👍👍👍
Ekdum sahi bhai.. shiddat se video banaya aapne 😁😁😁
Bro believe me , your climaxes are way more interesting than those of hollywood and bollywood movies.
Great explaination sir !!!
very nice explanation sir
please note that once transformation is performed using ColumnTransformer then that column shifts to the beginning and thus index of every column shifts. So for further transformation just keep this in mind while assigning index to columntransformer
yeah bro then how to prevent it
Yes I am also facing same problem, while applying column transformer our correct indexing
Doubt : The purpose of cross validation is to train the model on "whole dataset" in "folds", then why are we passing X_train (Already splitted dataset ) for cross_val_score(). Shouldn't it be X,y instead of X_train and Y_train. Sir, please explain. Also let me know if my thinking is wrong.
Actually, while using cross val score you pass the entire X and y
@@campusx-official How will you pass the entire dataset? The entire dataset will have missing values. And imputing missing values before the train test split leads to a data leakage problem.
This is my take from cross_val_score () ...when you do random test_train_split() for a dataset the algorithm would give accuracy of of model the random data which it splits as training data and test data ..but how can you say the data which was the part of the training dataset was best to train the model?... there is no way to say to model pick rows 10 to 100 for training as its best and 1 to 10 for test ..hence this things we use it cross_val_score() and we would definitely give cross_val_score() the entire independent column and dependent column data no splitting of train and test is done for this..
@@aination7302 We will pass entire X and y while doing the cross validation. And while doing that we pass pipe ( our pipeline consists of imputation,one,scaling,feature selection , model) , X ,Y,cv=5,scoring) ....so in this step our X and Y columns in dataset passes through the pipeline and so X and Y get through all the process .
So ultimately no, matter of data leakage may come as because One of the best ways to get rid of data leakage is to perform k-fold cross validation where the overall data is divided into k parts. After dividing into k parts, we use each part as the cross-validation data and the remaining as training data. Better to use k fold cross validation or Stratifiedkfold.
I have never seen this type of knowledge before. Thanks for sharing 🥰
Thank you sir, you are really amazing.
Thanks for making this video its really help me....
Excellent video and well presented!!
Very Useful
Thank You😀
mzaaa aa gya sir 17000 dekr mujhe itna acche se nhi padhya gya tha
superb lecture
Sir,u explain wonderfully
Nitish Sir in this video, while using transformers for the same test input we got result as 1 in pipeline and 0 in normal way. two diff outputs, iam not knowing which one to interpret as correct. Hope you answer this.
sir your THE BEST TEACHER
you sr are the best!
Brilliant 🔥
I have a big one question: What is the difference of build a Machine Learning application with Pipeline and to build a machine learning application with a OOP technique? I see that it is the same.
Pipeline ko agar khud se explore karna hai toh ek baar model ko streamlit ya phir flask me dalo phir tumhe pata chalega ki bina pipeline ko use kiye kitna code likhna padta hai aur agar pipeline use karte ho toh easily ho jata hai😊
great video
acche se samajh aaya
Really great demonstration