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.
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.
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 .
Correction :- trf1 se jo output aayega ... Vha columns k index change ho jayenge and in trf2 ... According to output from trf1 ... Sex col index = 3 and imputed_embarked col index = 1 ... So in trf2 use [1,3] instead of [1,6]
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 ❤️
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.
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
Interestingly, without Pipeline prediction is that person will survive, however with Pipeline the prediction is that the same person will NOT survive...Could you please explain this behavior??
Best videoes of the RUclips..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....🙏
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
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 RUclips. 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 RUclips 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 RUclips without any fee. Kind Regards Aqil Saboor
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.
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
Sir, You have made a very big mistake in this video. When your input passes through "trf1" the ouput indices changes such that the categorical column "Sex" and "Embarked" is now not in the same index as it was in the dataframe(in dataframe it was on 1,6) but now after "trf1" it will be on index number [1,3], so when you will check the output shape after "trf2" it will be (712, 236) it has actually encoded numerical column that is why we are getting 236 columns., But when you will actually see the output after all the transformation you will see that "sex" and "embarked" are still not encoded. The reason you are not getting error is because in third tarnsformation "trf3" you are slicing from (0,10) means selecting 10 columns out of 236 and all those 10 columns which you have sliced from 236 are in numerical form and thus you are not getting "sex" and "embarked" columns which are still not encoded and is in string format. I have copied your notebook and correctly indexced "trf2" which is [1,3] not [1,6] and just by changing this small thing my val accuracy is 80% and your's is 63%. ps: Although your concepts are very clear and its not a big deal to make such a tiny mistake considering that you have created tons of videos on different concept. Thanks a lot.
^ This comment should be pinned so everyone knows about this bug. Spent a long time trying to figure out why there are 200+ columns The column transformations change the order of columns so in subsequent steps we cannot rely on the initial indices. Columns order before 'trf1' - [Pclass, Sex, Age, SibSp, Parch, Fare, Embarked]. trf1 has imputer which works on Age, Embarked - so these 2 move to front. Column order after 'trf1' - [Age, Embarked, Pclass, Sex, SibSp, Parch, Fare] - hence 'trf2' needs to be given [1,3] for OneHotEncoding
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😊
Bhaiyya When I am doing pipe.fit(x_train,y_train) then this error is poped up " Cannot clone object. You should provide an instance of scikit-learn estimator instead of a class."
But how will it handle droping columns ; Theres nothing in pipeline where it drops useless columns automatically , as during testing here we are only providing required values in test not all the columns as present in original dataset.
ab toh mai kyaa karunga concatinate karunga ," Kyoh ki mai gareeb huuu" , kuch nahi kar saktha ....😂😂😂 i was listening the class with intensity you made me laugh many times. Im in day 29. On my 100 th video ill do my guru dakshina for sure . huge respect to you guru ji
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 " ,
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.
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.
what a beautiful way to explain such a complicated topic...hats off to your efforts Sir. Love allots💗💗💗
Exactly
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.
Man , your style of delivering the content is awesome !
you are making my journey a bit easier evryday .Thank you so much sir
I was lost like hell and here comes another great video with up to the core explanation.
Well explained. Nowhere I found such a great detailed explanation like this. Thank You so much.
I really like your explanation, Please keep uploading more videos.
Excellent code understanding and best teaching ever on RUclips✨🔥
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 channel is awesome no one can bit of this man
one of the best data science channel
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 .
Correction :- trf1 se jo output aayega ... Vha columns k index change ho jayenge and in trf2 ... According to output from trf1 ... Sex col index = 3 and imputed_embarked col index = 1 ... So in trf2 use [1,3] instead of [1,6]
Thankyou bro....Can you please explain how and why the index of the columns are getting changed?
Yeah I am confused about that too
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 ❤️
This video is literally gold brother for fresher Data scientists
sir I have never enjoyed learning ML before...You're actually the best!!
thanks sir so much
you don't know
how much you are important your lectures for me
thank youuuuuuu! doing a bootcamp abroad (paid 8Lakh) .. understood nothing! this video is giving me more clarity than the bootcamp ever could!!
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.
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
Nitish Sir, No one is better than you in teaching ML topics.
Interestingly, without Pipeline prediction is that person will survive, however with Pipeline the prediction is that the same person will NOT survive...Could you please explain this behavior??
cause in pipeline we used feature selection step which reduced the accuracy
I love the way you teach my brother.
Love you from Bangladesh.
Thank you so much for the playlist
Thanks a lot for this playlist. You have literally explained it so well and especially from the real project perspective. Thanks.
Teaching style is so good. I have learnt so much by you. Hats off to ur effort sir.
Sir, these videos are really helping me to understand ML concepts. Thank you so much, sir.
I don't know how are u, but thanks brother I loved your videos.... you make not only day every day to me
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
Ur incredible.... the way you teach..... simply amazing.... thank you for your efforts... giving you in free cost...
Best videoes of the RUclips..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....🙏
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
thanks for giving such good content. I don't think anyone is teaching in the same way from base
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 RUclips. 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 RUclips 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 RUclips without any fee.
Kind Regards
Aqil Saboor
whats the update of your interview ?
20:48 with pipeline
Brilliant brilliant brilliant 👏👏👏👏
We are blessed to have ...teacher like, Nitish sir!!
Ekdum sahi bhai.. shiddat se video banaya aapne 😁😁😁
We need more teachers like you
You are doing great work, just a suggestion change website address in description to recent one. Thanks for the video.
very Nice respected sir .kash aesay teachers hamari univaresties main bhi hotay.love from pakistan
bro, that was superb.
I loved the way you teach,... that granularity, hats off.
Thank a lot for your efforts, one of the best video I was looking for from long time.
Bro believe me , your climaxes are way more interesting than those of hollywood and bollywood movies.
awesome. No doubt best way of teaching. Complete road map got covered.
Best Explained i learned alot ,You are the best Teacher ❤
God bless you
Thank you sir for this amazing lecture
thank you sir
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.
amazing video sirji!!
Best Channel to learn data science.... :)
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
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.
You are Amazing Sir
Excellent Sirji,Hats off to you.
You are a legend buddy. Brilliant & thanks a billion.🙏🙏🙏🙏🙏
hatsoff sir! You are god at making concepts clear
You are an awesome teacher!
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
you sr are the best!
Its almost took Two days for me to study , understand the concept, code & implementation .. Weird but interesting ..
Sir, You have made a very big mistake in this video. When your input passes through "trf1" the ouput indices changes such that the categorical column "Sex" and "Embarked" is now not in the same index as it was in the dataframe(in dataframe it was on 1,6) but now after "trf1" it will be on index number [1,3], so when you will check the output shape after "trf2" it will be (712, 236) it has actually encoded numerical column that is why we are getting 236 columns., But when you will actually see the output after all the transformation you will see that "sex" and "embarked" are still not encoded. The reason you are not getting error is because in third tarnsformation "trf3" you are slicing from (0,10) means selecting 10 columns out of 236 and all those 10 columns which you have sliced from 236 are in numerical form and thus you are not getting "sex" and "embarked" columns which are still not encoded and is in string format. I have copied your notebook and correctly indexced "trf2" which is [1,3] not [1,6] and just by changing this small thing my val accuracy is 80% and your's is 63%.
ps: Although your concepts are very clear and its not a big deal to make such a tiny mistake considering that you have created tons of videos on different concept. Thanks a lot.
hi can you provide me with the code that works coz im just getting startted and im not able to figure out the cause of the error in my code.
can you please share your code or notebook?
^ This comment should be pinned so everyone knows about this bug. Spent a long time trying to figure out why there are 200+ columns
The column transformations change the order of columns so in subsequent steps we cannot rely on the initial indices. Columns order before 'trf1' - [Pclass, Sex, Age, SibSp, Parch, Fare, Embarked]. trf1 has imputer which works on Age, Embarked - so these 2 move to front. Column order after 'trf1' - [Age, Embarked, Pclass, Sex, SibSp, Parch, Fare] - hence 'trf2' needs to be given [1,3] for OneHotEncoding
can you tell me how to check the shape after trf2 ?
why we use reshape(1,1) or reshape(1,-1)
So beautiful explain all the things sir... salute to 🎯🤗👍
really appreciate your work..Just started learning with you.
Brilliant 🔥
awesome ! thanks for going in to so much detail so that you can make it easy for us
very nice explanation sir
Wow man... I am running short of words.
Very informative, I will work on my next proeject with pipeline.
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
thank you for giving such kind of knowledge, your awesome sir❤
Amazing way of teaching!!
superb lecture
God level teaching!
Thank you sir, you are really amazing.
Your videos are excellent . Very easy to understand. Do you have a video on deployment of model to AWS EC2 ?
ruclips.net/video/_rwNTY5Mn40/видео.html
pretty straight and neat explanation
Excellent video and well presented!!
Nice Explaination
Excellent Explanations 👍👍👍
So Fantastic so amazing just like wooooow!!!!!!!!!!!
Sir! First half pura samajh ne ka koshish kiya....par brain mein locha hoagaya!😆....Par overall mamla samajh gaya🤌
Bhaiyya When I am doing pipe.fit(x_train,y_train) then this error is poped up " Cannot clone object. You should provide an instance of scikit-learn estimator instead of a class."
acche se samajh aaya
Sir,u explain wonderfully
Thanks
Great explaination sir !!!
This is amazing , nitish sir !!
Well explained sir...thank you
But how will it handle droping columns ; Theres nothing in pipeline where it drops useless columns automatically , as during testing here we are only providing required values in test not all the columns as present in original dataset.
ab toh mai kyaa karunga concatinate karunga ," Kyoh ki mai gareeb huuu" , kuch nahi kar saktha ....😂😂😂 i was listening the class with intensity you made me laugh many times. Im in day 29. On my 100 th video ill do my guru dakshina for sure . huge respect to you guru ji
Thanks for making this video its really help me....