I like it how everyone is calling him Sir like this is a classroom. Good simplified explanation of pipelines. Doesn't have to be more complciated than this.
I was struggling to understand what exactly is a pipeline in ML but you explained it so concisely. I am so grateful to you sir. You just earned a new subscriber. Regards from Singapore.
Hi, can you please help me where to learn mlops in Aws using Sagemaker? Do you have real-time usecases / videos that I can refer to learn for practical approach? Thanks!
Hi Sir, i am bit confused with the terms EDA; Feature Engineering; Data Wrangling or Data Munging... also Data Cleaning is a part of EDA or not... i did read lot of articles or checked videos... and evey ne has their own way of defining all these... but if we do the conclusion then all these solves the same purpose but just called with different names. Please have a video to explain them atleast in short descriptions.
Hi Mayank, thanks for your question: EDA - Exploration of Data, ex- How many male and female in given data... FEATURE ENGINEERING - Deriving new features from data. Ex- Deriving your recency in number of days on flipkart from your last visit date. Data wrangling and munging - making data ready for consumption in lower layers like analytics - Example - Joining all "Shoppers stop" data throughout india to do "shoppers stop" revenue forecast. Data cleaning - A phase in Data science pipeline to clean the dirty data. Example - putting "Male" if gender is not available. Data cleaning is before EDA and after Data Import . More questions , feel free to join my live this sunday 4PM IST
I like it how everyone is calling him Sir like this is a classroom. Good simplified explanation of pipelines. Doesn't have to be more complciated than this.
Thanks a lot. Your comments are my motivation.
I was struggling to understand what exactly is a pipeline in ML but you explained it so concisely. I am so grateful to you sir. You just earned a new subscriber. Regards from Singapore.
It couldn't have been simpler for someone as new as me to Data Science and ML. Thanks Sir... :-)
Thanks Aryan.
The best video I saw about Pipeline (and I spent the last week seeing videos!) Thank you :D
Glad it is helpful for you
Thank you for simple explanation.
Welcome :)
Good information
Thanks for watching :)
1:10 Reach Your Goals
ruclips.net/video/iCGqeufhI9k/видео.html
Sir , when you will upload the practical pipeline implementation video?
It will be uploaded this Friday 5PM Ist.
@@UnfoldDataScience thankyou sir , waiting 🥳
Thank you for existing!! 👏🏼
Your comments are my treasure, thanks a lot.
Very helpful. Thank you sir. (from philippines here
Very informative Sir, eagerly waiting for the next video
Thanks Harsha, Sure :)
Nice
Sir where is less Work load , less work Pressure & less Working Hrs. - as a Data Analyst or as Machine learning Engineer or as a Data Scientists ?
Very nicely explained
Thanks Vivek.
Nice Video Aman. Thank you.
Thank you Hitesh.
0:46 Write an essay quickly
ruclips.net/video/iCGqeufhI9k/видео.html
Thank you for this sir
Welcome.
Excellent explanation, you make simple and easy to understand. Thank you.
Thanks for the positive feedback. Please share with others as well.
Your content is simple and easy to understand. Thanks for your videos bro.
Welcome Divakar. Thanks for your valuable comment.
0:52 Reach Your Goals
ruclips.net/video/iCGqeufhI9k/видео.html
finished watching
Very clear explaination
Thanks a lot.
Thank You for the detailed stepwise explaination.
Welcome Suraj.
Waiting for the practical..👍
Sure Sourav.
1:35 Writing an essay is easy
ruclips.net/video/uGiR-neokWs/видео.html
A very relaxed and good explanation. Thanks for this video.
Thanks Amit.
Great video Sir, helped a lot.
Thanks for watching
very clear! thanks!
Welcome Luis. Merry Christmas.
thanks a lot for this, u helped me a lot
Yeah you really make this simple and easy to understandable.
Thanks a lot.
Request you to share my videos in various data science groups you are part of, that will motivate me to create more content :)
So helpful.. eagerly waiting for python pipeline flow video 💯
Thanks Bhusan, Sure :)
Hi, can you please help me where to learn mlops in Aws using Sagemaker? Do you have real-time usecases / videos that I can refer to learn for practical approach? Thanks!
Sir, Kindly make a vedio on Maximum Likelihood Estimation and Language Translation models
Already uploaded one video :
ruclips.net/video/5TczDUBOH74/видео.html
on Language models, I will create
Sir, can you make a video, where you will show practically step by step
Yes Krishnendu, Next Video :)
@@UnfoldDataScience sir, waiting for your next video ❤️🙏
Dhanyawad...🙏🙏🙏
Welcome.
Thanks
Welcome :)
💝👌
Thank you! sir, this was so helpful.
Glad it was helpful Preetam.
Good explanation, easy to understand ☺️
Glad you think so!
this video was really useful. Thank you so much!
You're very welcome Doug.
This was amazing, thank you sir!
Welcome Travis.
Hi Sir, i am bit confused with the terms EDA; Feature Engineering; Data Wrangling or Data Munging... also Data Cleaning is a part of EDA or not... i did read lot of articles or checked videos... and evey ne has their own way of defining all these... but if we do the conclusion then all these solves the same purpose but just called with different names.
Please have a video to explain them atleast in short descriptions.
Hi Mayank, thanks for your question:
EDA - Exploration of Data, ex- How many male and female in given data...
FEATURE ENGINEERING - Deriving new features from data. Ex- Deriving your recency in number of days on flipkart from your last visit date.
Data wrangling and munging - making data ready for consumption in lower layers like analytics - Example - Joining all "Shoppers stop" data throughout india to do "shoppers stop" revenue forecast.
Data cleaning - A phase in Data science pipeline to clean the dirty data. Example - putting "Male" if gender is not available.
Data cleaning is before EDA and after Data Import .
More questions , feel free to join my live this sunday 4PM IST
Sir , Is this called the ETL pipeline also ?
No Sourabh. It is different Pipeline.
Where is example of it?
Need practical examples
Sure :)
0:40 Writing an essay is easy
ruclips.net/video/iCGqeufhI9k/видео.html
really needed thanks sir ... please accept my connection on LinkedIn sir
Sure Arshan, thanks for watching :)
Sir plz check the msg on Instagram I share you a sentiment analysis project file link.
Ok Yagya. Will check