First time i am seeing krish sir wearing interviewer hat. After watching i guess you enjoyed a lot this process. I have just attended couple of interviews in past. But you both guys are so chill. Hope one day i will get the chance of sitting in front of you.
I think decision tree may not handle imbalanced data everytime. Because we are splitting each node using a metric(gini/info gain) .The goal of the split is to ensure the classes in each split of the node is very different. For example : We have 100 datapoints out of which 5 datapoints belong to one class. Now if the node splits as 50 :50 ratio and all the 50 classes of majority class is in one split . The info gain will be high but we will not be able to recognize those 5 samples of minority class. Yes, no doubt at some point those 5 classes will be classified but it will be deep tree with lots of levels in it. Which means we may overfit. So we can use class weights /upsampling /smite in this case.
Nice one,one suggestion can u write subtitles like correct answer if answered correctly and false if the student says wrong answer.it would be really helpful.
Pretty helpful.. Thanks for ur efforts.. But one suggestion here... Unlike real interviews where once candidate answers about his project etc they go in depth about that particular topic and then the interview opens up.. For example here when Krish sir asked about ticket level like P1, P2 etc priority levels the candidate said he didn't consider them which is a lil bit surprising considering all ticket system has an escalation level.. So there he could have gone in depth and would have asked what are the important features then and how he determined etc... Even when Sudhansu Sir asked which algorithm you have used he said Random forest regression but then he should have asked why this technique and preprocessing techniques etc. Rest everything was very informative but the only thing is in interviews from our projects only they go in depth and then it opens up. Keep up the good work and thanks for such fruitful sessions 👏
Bro 😅 what all things go in the mind of a candidate you don't know ... is the guy prepared or not you don't know so saying these things is easy while watching it but facing the same thing and doing a live interview in front of 700 people is not an easy task ... yeah I agree what you said is a good approach but it all comes when the mindset is set in such context okay this is going to like candidate is confused if its a open discussion or interview so yeah its tough to do these at live
And also for the outliers, if you have two or more hidden layers, it will be resilient to outliers and also input noise. You can very well check this by implementing it.
These interviews are making me overconfident! Lmao!! I'm still in second year! Looking forward to work even harder to get a job at a good product based company! These videos are really helpful!
I would have to disagree with the part where you had mentioned decision tree is not sensitive to imbalanced classes. It can handle the imbalanced classes well but still they are influenced by the imbalanced classes (information gain and the Gini measure are skew sensitive). You have to use a weighted decision tree or GBDT to tackle this issue.
No they’re not , there’s no such thing as formula coefficients on which they’re being trained , so it’s just kind of if else statement for every condition and imbalanced dataset is just going to get passed through these nodes
Why people jump directly to deep learning? In the last fresher interview, the guy didn't worked on machine learning algorithms and now this guy has only done computer vision part in deep learning? why this is happening? why everyone is not doing each & ever part of machine learning & deep learning? Just wanted to know that why people do this. I don't think so that it's a good practice
It's upon each person perspective it's not like learn machine learning and then go deep learning as deep learning is independent of machine learning shallow model algorithms... And Deep learning is quite more interesting than Machine Learning... The main thing is Krish sir is more towards Machine Learning so he always ask those type of questions... And the candidate is more towards deep learning so yeah what you see you will tell these guys don't know ML stuff and all if Krish sir would be asking DL question from the start then you would never make such opinion ...its what you see is always not true I would say.
Probably in companies they ask both ML and DL and that is what I have done. Candidates should focus on both. Otherwise they will not be able to decide whether to go with ML or DL and I am not always towards ML :)
One should learn everything but they can focus on one side only that interest them..its very broad stream and u cannot manage to know every detail in every domain. It comes with over year after experiencing many tasks.
@@krishnaik06 Thank u so much Sir.Your effort in making the step by step guide to start DataScience is really apparent .I have a doubt.When should i start with Kaggle (at which point during this excellent curriculum shud i start Kaggle)...Pls reply sir..... I'm expecting a reply from u sir
Thank u so much Sir.Your effort in making the step by step guide to start DataScience is really apparent .I have a doubt.When should i start with Kaggle (at which point during this excellent curriculum shud i start Kaggle)...Pls reply sir..... I'm expecting a reply grom u sir
Sir I am doing actuarial sciences, although it’s amazing but it’s mostly applied. I am particularly interested statistics in pure form because, whenever I apply, I would like to know that I know it’s reasoning from core. Please make a video recommending step by step guide through books how to study statistics from books in its pure form.
Hi Krish, I have seen 2-3 videos of yours and that motivated me to learn DS and ML. Currently I have C++ profile having 9 Years of experience and I am interested in Data Science and ML. Is there any scope that I can move into a DS and ML profile? How can I achieve this into 3-4 months?
