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
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 .
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.
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]
Best videoes of the RU-vid..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....🙏
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 ❤️
Hi Nitish, these videos on column transformers and pipelines are really amazing. Can you please also let me know how can I incorporate IQR based outlier detection using column transformer and pipeline? I tried doing it but was not able to get the desired results
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 RU-vid. 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 RU-vid 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 RU-vid 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 .
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
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, 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?
