01:49 (Q1) What are the assumptions for Linear Regression? 03:33 (Q2) What is the difference between Precision and Accuracy, can you explain in terms of Confusion Matrix and Confidence Interval? 06:36 (Q3) Normal distribution question 09:36 (Q4) What is the power of a hypothesis test? Why is it important? 11:33 (Q5) What is the difference between K nearest neighbors and K means? 13:19 (Q6) Explain Random Forest in Layman terms. 17:40 (Q7) What is K fold cross-validation? Why do we use it? 19:31 (Q8) Explain how we can handle missing values in our data? 22:21 (Q9) What is the difference between a Bar Graph and a Histogram? 24:17 (Q10) What is a Box and Whisker Plot and when should we use it?
Much appreciating your efforts. Useful video to get a fair idea on what diff type of questions one can face. I have one small observation as follow: Explanation of question 6, is more of a feature selection rather than how actually a random forest works. It should be first deciding some most important parameters (feature selection) and then asking people what is best out of 3 offers based on selected parameters. People will respond as per their experience and learning and recommend an offer to go ahead. The highest number of people recommending an offer is what I will go ahead. Now, in technical term, these number of people are my trees (forest).
16:00 Random Forest LinkedIn Example. There are 10 companies you got a job offer. You show 3 of them to your LinkedIn contacts. If change the example like this we consider both bagging and subspace sampling. (Bagging-Bootstrap Aggregation is to use some of the data, subspace sampling is to use some of the features. )
I like the way you tried it, but unfortunately you lack explanation , when you say linear relationship please be specific about linear relation between what? if i have a quadratic data, you mean i can't run linear regression?
Not sure about the explanation for question 1. Nor why linear regression requires the assumptions for best linear unbiased estimators. Can still fit a linear regression just fine without them, albeit it might invalidate confidence intervals and interpretability. Who cares what the assumptions are if MSE is low?
do you think the MSE will be low if the assumptions are not true? Low MSE in itself means that the residuals are small and the predicted linear regression line closely represents the actual pattern in data.
Aaa... You ggggggive excellent explanation.. Please use perfecttttttt mic .. Because a lotttt of unnecessary sounds coming from your mouthhhh.. Those are getting irritattttted me.
for someone who says "communication is key to data science" you seem to use the visual medium of video very poorly... Reading exactly what's on screen 10x faster than you reading it out really bugged me. I realize actual video can take a lot longer to make, or cost a lot more. But PowerPoints with text dumps haven't been 'good communication' since 2000. Sorry if this was harsh, great info. I just muted it and scrolled through each slide - which would've been a better experience on slideshare sadly.
01:49 (Q1) What are the assumptions for Linear Regression?
03:33 (Q2) What is the difference between Precision and Accuracy, can you explain in terms of Confusion Matrix and Confidence Interval?
06:36 (Q3) Normal distribution question
09:36 (Q4) What is the power of a hypothesis test? Why is it important?
11:33 (Q5) What is the difference between K nearest neighbors and K means?
13:19 (Q6) Explain Random Forest in Layman terms.
17:40 (Q7) What is K fold cross-validation? Why do we use it?
19:31 (Q8) Explain how we can handle missing values in our data?
22:21 (Q9) What is the difference between a Bar Graph and a Histogram?
24:17 (Q10) What is a Box and Whisker Plot and when should we use it?
thanks hero
Who are you
These are great! This goes really well with a video I made about what to expect from the data science interview process. Keep up the good work!
Much appreciating your efforts. Useful video to get a fair idea on what diff type of questions one can face. I have one small observation as follow:
Explanation of question 6, is more of a feature selection rather than how actually a random forest works. It should be first deciding some most important parameters (feature selection) and then asking people what is best out of 3 offers based on selected parameters. People will respond as per their experience and learning and recommend an offer to go ahead. The highest number of people recommending an offer is what I will go ahead. Now, in technical term, these number of people are my trees (forest).
Great video and great topics you propose for interviews! Thank you.
I was asked about some ML and deel plearning algorithms, about accuracy recall f1 score and precision. Also about testing... some iq questions 2....
GREAT QUESTIONS
Please do more interview videos. This is great help. Thank you!
Very helpful video. And I loved your explanations and advice! Keep posting videos, I enjoyed learning with you!
Please more vids on interview questions! This was great!!
It was very helpful video.
Keep posting videos on data science and how to answer them
Um, the way you explained precision and accuracy was so good, I can rest now 😅
0
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I appreciate your attempt 👏👏
Appreciate Your efforts.
Thank you.
Keep posting 🙏
Excellent questions , looking forward with more questions
i am working on a new one right now. Should be out in couple of days. thanks for commenting.
16:00 Random Forest LinkedIn Example. There are 10 companies you got a job offer. You show 3 of them to your LinkedIn contacts. If change the example like this we consider both bagging and subspace sampling. (Bagging-Bootstrap Aggregation is to use some of the data, subspace sampling is to use some of the features. )
Great work. Thanks
Your voice modulation is very audible and soothing.. Thanks for the video..
Thank you ! We need more videos based on data science interview questions.
Thanks for sharing this video really helpful for new students.
Best Random forest ever!.. Thank you so much!
Thanks for the work... Good efforts
You have done a good job. It is very helpful
Great stuff!
Good content and explanation..
Thanks for sharing!!!
Appreciate the video!
Really appreciate 😊
Very helpful
Very well explained ! Thank you.
Great job
good one please domore videos on layman term explanations
Thank You Ma'am !
Good Work 😁
Very helpful video
Great efforts 👌👌👌💐💐💐 please do post more videos. Its was very useful
thank you!
thank you very good questions
welcome!
Thankyou so much 💖
Hi, can you please tell me why is multicollinearity bad? Thanks in advance :)
Bar graph gives us insight about categorical data. I think Discrete is not the right term.
In Z score question can we do it with a t test
👍
If it deviates from the standard more than (k) times , the probability of the z score is squared exponentially
I like the way you tried it, but unfortunately you lack explanation , when you say linear relationship please be specific about linear relation between what? if i have a quadratic data, you mean i can't run linear regression?
Not sure about the explanation for question 1. Nor why linear regression requires the assumptions for best linear unbiased estimators. Can still fit a linear regression just fine without them, albeit it might invalidate confidence intervals and interpretability. Who cares what the assumptions are if MSE is low?
do you think the MSE will be low if the assumptions are not true? Low MSE in itself means that the residuals are small and the predicted linear regression line closely represents the actual pattern in data.
Are these questions for freshers? Or experienced one as well?
for freshers
you are indian and smart. good news for you and india
Aaa...
You ggggggive excellent explanation..
Please use perfecttttttt mic ..
Because a lotttt of unnecessary sounds coming from your mouthhhh..
Those are getting irritattttted me.
These questions are way way way too easy for any good ds job. These are more like statistics 101 problems.
for the sake of it, please use a different, more pleasant computer voice for your videos. Thanks in advance.
Not engaging.
no offense-- your content seems to be great; but it's very tiring to listen to you're voice-over. sorry, but TRUE
idiot be grateful and show some intelligence and put it on 1.5x , use equalizer if you can't focus
for someone who says "communication is key to data science" you seem to use the visual medium of video very poorly...
Reading exactly what's on screen 10x faster than you reading it out really bugged me.
I realize actual video can take a lot longer to make, or cost a lot more. But PowerPoints with text dumps haven't been 'good communication' since 2000.
Sorry if this was harsh, great info. I just muted it and scrolled through each slide - which would've been a better experience on slideshare sadly.
I think you should listen too. It's a fabulous video.
@@JatinderSingh-ff4eo It's good content sure - no arguments. Not what i was getting at.
great then i guess my video will be useful for those with hearing problems if my slides are good enough to just read thru.. ha ha :-)
Very helpful