Pipeline In Machine Learning | How to write pipeline in machine learning

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  • Опубликовано: 8 ноя 2024

Комментарии • 85

  • @drakZes
    @drakZes 4 года назад +6

    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.

  • @rohitg1392
    @rohitg1392 Год назад +1

    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.

  • @aryansinha301
    @aryansinha301 3 года назад +4

    It couldn't have been simpler for someone as new as me to Data Science and ML. Thanks Sir... :-)

  • @nachoeigu
    @nachoeigu 2 года назад +1

    The best video I saw about Pipeline (and I spent the last week seeing videos!) Thank you :D

  • @RamanKumar-ss2ro
    @RamanKumar-ss2ro 4 года назад +2

    Thank you for simple explanation.

  • @preranatiwary7690
    @preranatiwary7690 4 года назад +1

    Good information

  • @carryminatifan9928
    @carryminatifan9928 4 года назад +3

    Sir , when you will upload the practical pipeline implementation video?

  • @shaimakhlaief3187
    @shaimakhlaief3187 Год назад

    Thank you for existing!! 👏🏼

  • @froilanemeliano6551
    @froilanemeliano6551 2 года назад

    Very helpful. Thank you sir. (from philippines here

  • @Sumit_Harsha
    @Sumit_Harsha 4 года назад +1

    Very informative Sir, eagerly waiting for the next video

  • @arpittrivedi6636
    @arpittrivedi6636 Год назад

    Nice

  • @Mrroy08657
    @Mrroy08657 Год назад

    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 ?

  • @shine_through_darkness
    @shine_through_darkness 3 года назад +1

    Very nicely explained

  • @hiteshyerekar2204
    @hiteshyerekar2204 4 года назад +1

    Nice Video Aman. Thank you.

  • @tomisinakinfemiwa6921
    @tomisinakinfemiwa6921 3 года назад +1

    Thank you for this sir

  • @ole4190
    @ole4190 2 года назад

    Excellent explanation, you make simple and easy to understand. Thank you.

    • @UnfoldDataScience
      @UnfoldDataScience  2 года назад

      Thanks for the positive feedback. Please share with others as well.

  • @divakarjona7603
    @divakarjona7603 4 года назад +1

    Your content is simple and easy to understand. Thanks for your videos bro.

    • @UnfoldDataScience
      @UnfoldDataScience  4 года назад

      Welcome Divakar. Thanks for your valuable comment.

    • @topprofitstrategy4747
      @topprofitstrategy4747 4 года назад

      0:52 Reach Your Goals
      ruclips.net/video/iCGqeufhI9k/видео.html

  • @sandipansarkar9211
    @sandipansarkar9211 3 года назад

    finished watching

  • @prettylilnerdy6802
    @prettylilnerdy6802 3 года назад

    Very clear explaination

  • @surajpandey86
    @surajpandey86 3 года назад

    Thank You for the detailed stepwise explaination.

  • @souravbiswas6892
    @souravbiswas6892 4 года назад +1

    Waiting for the practical..👍

  • @AmitabhSuman
    @AmitabhSuman Год назад

    A very relaxed and good explanation. Thanks for this video.

  • @isaiperez3130
    @isaiperez3130 2 года назад +1

    Great video Sir, helped a lot.

  • @luisdotto7944
    @luisdotto7944 2 года назад +1

    very clear! thanks!

  • @marg474
    @marg474 2 года назад

    thanks a lot for this, u helped me a lot

  • @muhammadarqamwaheed186
    @muhammadarqamwaheed186 3 года назад

    Yeah you really make this simple and easy to understandable.

    • @UnfoldDataScience
      @UnfoldDataScience  3 года назад

      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 :)

  • @bhushanchaudhari378
    @bhushanchaudhari378 4 года назад +1

    So helpful.. eagerly waiting for python pipeline flow video 💯

  • @sampathkumarbasa4774
    @sampathkumarbasa4774 2 года назад

    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!

  • @piyalikarmakar5979
    @piyalikarmakar5979 3 года назад

    Sir, Kindly make a vedio on Maximum Likelihood Estimation and Language Translation models

    • @UnfoldDataScience
      @UnfoldDataScience  3 года назад

      Already uploaded one video :
      ruclips.net/video/5TczDUBOH74/видео.html
      on Language models, I will create

  • @krishnendubhowmick481
    @krishnendubhowmick481 4 года назад +1

    Sir, can you make a video, where you will show practically step by step

  • @AIRobotica
    @AIRobotica 4 года назад

    Dhanyawad...🙏🙏🙏

  • @Technicalchurn
    @Technicalchurn 4 года назад +1

    Thanks

  • @saiharshareddy8808
    @saiharshareddy8808 2 года назад

    💝👌

  • @preetamchaurasia6675
    @preetamchaurasia6675 3 года назад

    Thank you! sir, this was so helpful.

  • @madanmanikumar
    @madanmanikumar 3 года назад

    Good explanation, easy to understand ☺️

  • @nolimit167
    @nolimit167 3 года назад

    this video was really useful. Thank you so much!

  • @travisratnaharan4476
    @travisratnaharan4476 2 года назад

    This was amazing, thank you sir!

  • @mayanktripathi4u
    @mayanktripathi4u 4 года назад

    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.

    • @UnfoldDataScience
      @UnfoldDataScience  4 года назад +4

      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

  • @sourabhagarwal4852
    @sourabhagarwal4852 3 года назад

    Sir , Is this called the ETL pipeline also ?

  • @learn_techie
    @learn_techie Год назад

    Where is example of it?

  • @jojushaji3010
    @jojushaji3010 4 года назад +2

    Need practical examples

  • @MAK335
    @MAK335 4 года назад +1

    really needed thanks sir ... please accept my connection on LinkedIn sir

  • @yagyarajbhatta2444
    @yagyarajbhatta2444 4 года назад +1

    Sir plz check the msg on Instagram I share you a sentiment analysis project file link.