Title | : | Complete Machine Learning In 6 Hours| Krish Naik |
Lasting | : | 6.37.52 |
Date of publication | : | |
Views | : | 412 rb |
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All the materials are given below githubcom/krishnaik06/The-Grand-Complete-Data-Science-Materials/tree/main Comment from : Krish Naik |
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39:30 Comment from : Pankaj Kumar |
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Error rendering embedded code
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brInvalid PDFbrgetting this msg while accessing some of your pdfs sir Comment from : yogit gurram |
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sir i hope this reaches you and you reply me pls that i have a doubt that in your complete ml playlist which consists of 153 videos , i have seen some of the videos where you have explained what is what and mathematical concepts behind that and i need to ask whether the code implementation part of every algorithm is also there in that playlist or what do you suggest for code implementation Comment from : yogit gurram |
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is this for begginers? Comment from : Shubham khandelwal |
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❤ Comment from : RAJAT CHAUHAN |
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Best Machine learning content out there😊😊😊 Comment from : Code With EmmaPrime |
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Hi Sir, Can you please help me with the content notes? I tried accessing the given link but it is not working Comment from : Jaimin Sagar |
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Really great content right here; from the rudiments to the practical application is covered here regarding all the traditional ML Algorithms! Just Amazing Period Comment from : Adnan Hassan |
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is this the complete machine learning?? Comment from : Vazahat Qureshi |
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Sir in your machine learning playlists of Hindi and English have vast difference in quality as well as quantity, why so? Comment from : Nandit Sharma |
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5:06:42 Comment from : kartikey Bartwal |
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very nice voice, no confusion for listening Comment from : Padhai Dot Com |
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Can we see only this video before placement?? Comment from : Lisha Sharma |
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48:24 Didn't understand Can someone elaborate?? Comment from : Arindam Phatowali |
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Can you please share these slides, if already done can anybody tell?? Comment from : Ajay babu Patel |
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while you might encounter Gini impurity values higher than 05 in the context of the Iris dataset, this is due to the multiclass nature of the problem and the specific calculation used for multiclass Gini impurity It doesn't imply that the maximum impurity for multiclass problems is 05; that limit applies to the binary case Comment from : jiya |
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1:13:13 Underfitting me High-bias & Low-var aayega @krish naik Comment from : HuNting Boy |
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I don't understand at around @2:32:00, why are we not using Linear Regression directly, as there's no case of overfitting Let me know if I make sense here, but overfitting would be there if our training model had shown 100 accuracy We haven't checked that, and are using ridge and lasso Please help me understand I believe we should only have used Linear Regression for our purpose Thanks Comment from : Sanchit Agarwal |
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Sir material link of this course has expired can u plz provide us a fresh link for annotation notes of what u have explained in this video Comment from : Ashutosh Anand |
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is this video enough to crack data science interviews? Comment from : Kavi-learn |
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Explanation of logistic regression was the most awesome explanation that i ever foundThank you for the session Krish Comment from : Mani Ratnam |
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Watching it to revise my concepts before my internal exams I have exams in 7 days Comment from : 69 Ashish Yadav |
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Wah Wah - Excellent Video Comment from : Qasim Khan |
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I am watching this video now but could not fetch this boston housing prices data set as sk learn maintainers are telling us strongly not to use this datasethow can i complete this tutorial now??@krishnaik sir Comment from : Bajrang Sharma |
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amazing content! but ads after every 5 mins is a bit annoying and distracting!! Comment from : Saksham Singh |
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great explanation of so many algorithms in a short time Comment from : Tarab alam |
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will it be possible to get the slides in notes format? Comment from : Lovedeep Singh |
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Please share the prerequisite for this video anyone Comment from : Krish Kumar |
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Please provide the pdf notes of the lecture Comment from : Tasawwur Ahmad |
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Sir , please upload NLP tutorial for beginners Comment from : jai shivaji |
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😂 Comment from : Technology World |
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Definitely good and great refresher who has exposure in ML,STATS and MATH(calculus and Algebra)but not for absolute beginners , if you want to learn ML without prior knowledge, Andrew's course in coursera is the best, you can audit the course for free over there Comment from : jagan karukonda |
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Your presesentation and teaching is excellent! Comment from : Busuyi Alabi |
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Sir, the link you have given to access the notes is not working Can you update the notes link Comment from : Sheraphine Shovan M |
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Thank you Krish, this is very helpful I'm beginner, is it possible to get the notes of the video? Comment from : genai142 Kumar |
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Perfect Ml video all over YouTube You're explanation is just amazing 🤩 Thank you so much ( I'm now only at the beginning😅 many more to go ) Comment from : zeroxia |
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6hrs ago, I don't know machine learning 💀💥 Classic✨ Comment from : Navaneeth Stark |
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sir i cant find the materials in the below linkbut the the video is just awesome Comment from : Oishik Das |
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i hate ads on this channelbr Comment from : sangeet |
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Thoroughly enjoyed the videos I was able to get over the fear of learning ML as it made my learning process smooth Thank you ❤️ Comment from : Tripta Bhattacharjee |
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Got placed in TCS Research and Innovation Lab by just watching your videos 🥳🥳🥳 Thanks for making these awesome videos @Krish Naik❤ Comment from : Rashid Imam |
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given link for the materials is not working Comment from : Maaz Ashhar |
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wow! very useful content❤❤ Comment from : A2Y Automobile |
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thank you sirrr Comment from : Priyank naik |
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timestamp 1:06 Comment from : Anshul Raina |
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AWESOME! Comment from : Saheli Dutta |
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It is in which programming language? Comment from : Strange Things |
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anyone have the notes because the website is not opening Comment from : Rahul |
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My stamp @24:00 Comment from : J M |
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Thank You Sir, for this vdo Comment from : sudhir mallick |
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3:13:37 bhaiyaku loosu pudichirchu Comment from : ONLY HUMAN |
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just now completed full video until 6:37:51 video in 2X mode Tq Guruji Comment from : vamsi krishna ravilla |
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I am unable to understand what you are teaching it looks very difficult for me Comment from : Vinod Yadav |
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Editing tool name? (Black screen tool name please) Comment from : Sanjay Makwana |
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xgboost - similarity calculation is wrong the numerator is sum of squares and not square of sum Comment from : TelaKovela |
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Great video! I think your Bias definition is backwards @5:38:37 Comment from : Ryan Ondocin |
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Can somebody tell me from where will I get the notes of this video? Comment from : Sudhanshu Bhardwaj |
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Why 1/2m? If we differentiate the value "2" should come at the numerator But here the value has come at denominator? Can anyone answer this for me please? Comment from : PAVAN KUMAR |
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5:38:29,please clarify on the statement how model has high bias when its performing well on training data Comment from : rafi basha |
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3:54:54 hmmm wait what? Comment from : k201083 Muhammad Rayan Ali |
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16:08 Based on customer segmentation later on we can decide classification or regression please someone explain this Comment from : Syed Siddiq |
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I wish i could give 10000 likes to this video Comment from : tushar Singh |
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Hello Krish!! Can't download study material from your given link please check Comment from : Hema yogi |
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thank you soo much sir for your great explanation Comment from : srikanth nimmala |
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You are the best teacher 🥰 Regards from Malaysia Comment from : Rosnawati Abdulkudus |
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your material link is not exist nowplease krish sir send new link to study this lecture please krish sir this the humble request Comment from : the facts |
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Do anyone have course materials, as given link in description for materials is not working Comment from : Govind Choudhary |
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Thank you very much Comment from : Onkar Patil |
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sir can u provide the new materials link Comment from : ash B |
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❤️❤️❤️❤️ Comment from : Keerthivasan Sundararaman |
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Thank you, Krish, for such an incredible Tutorial, Have you made all the PDF files available? Comment from : Ewnetu Abebe |
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What an amazing tutorial ever seen, Thank you, Krish, but Have you put all the pdf materials kindly Comment from : Ewnetu Abebe |
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so many ads yaar Comment from : tushar mehta |
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👏👏🙏 Comment from : KillingIsEasy |
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15:45 Comment from : Rishav |
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Hi @krish can you please provide the notes of this session Comment from : Charan Gowda M N |
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- 18:16 Comment from : Sourav Samant |
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Could you mind to share the notes you have used for this amazing video I have ever seen in the internet Please as It will help us a lot to go with your lecture I think Comment from : Mohammad Ashraful Hoque |
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2:48:18 Comment from : kushagra |
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Material link not working,please fix it Comment from : v |
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Can you please share the notes prepared during this session, it will be enough for a quick revision Comment from : MD MUZAKKIRHUSSAIN |
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Thank you so much sir Comment from : Nikita Sinha |
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nots link is not working Comment from : Aryan Singh |
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Bookmark : 31:00 Comment from : Suparno Banerjee |
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Can't thank you enough for this! Comment from : Anomitra Dhar |
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Why discuss gradient descent in regression when we have OLS estimators Its just unnecessary Comment from : PRAJNESHA RAJ NARAYAN SINGH |
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how 3 at 1:18 it has to be 4 Comment from : sai charan pappala |
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👍 Comment from : MD ALAM |
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Sir, the materials are no more accessible Please check Comment from : rishav kumar |
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Aap kis caste ke ho sir mey bhi naik hun Comment from : GEXTG |
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Just a question if any of could reply : for Naïve Bayes the feature and target value has to be categorical? Comment from : niladri biswas |
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