Interview Questions On Feature Scaling | normalization vs standardization machine learning

Interview Questions On Feature Scaling | normalization vs standardization machine learning
#FeatureScalingInterviewQuestions #UnfoldDataScience
Hello ,
My name is Aman and I am a Data Scientist.
About this video:
In this video, I explain about Interview Questions On Feature Scaling and normalization vs standardization machine learning. I explain what are the various areas in which questions can be asked during machine learning interview. I explain questions and answers for feature scaling. Below questions are answered in this video:
1. Interview Questions On Feature Scaling
2. Normalization vs standardization machine learning
3. Feature scaling in machine learning
4. Min Max scaler vs Standard Scaler
5. data scaling and normalization in machine learning,
6. feature scaling example
7. feature scaling explained
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[@pratiknaikwade95]

very helpful # Data Science Interview questions and answers

[@mayamathew4669]

Very helpful video.Thanks Aman.

[@krishnabhadke6161]

Thank You Sir , simple and clear as always

[@ashwinitekude5269]

Nice Tutorial....
Thanks a lot

[@terryterry3733]

I always follow your classes regularly. i always miss SVM ( hard and soft ) lectures can u pls make videos on that. there is no much information available in RUclips ...

[@UnfoldDataScience]

Sure.

[@terryterry3733]

@@UnfoldDataScience thanks sir i will wait

[@souravbiswas6892]

Excellent explanation. I started watching your videos when you had 1k subscribers. Now it turned into 35k. It's pure hard work. Keep it up and thank you.

[@syednizamuddin9855]

thanks sir, the way you explain complex topics in simple terms is amazing

[@UnfoldDataScience]

Welcome Syed, pls share with friends as well.

[@manaspradhan2166]

Nice Video Aman, very detailed explanation

[@UnfoldDataScience]

Thanks Manav. Please share with others as well who could be benefited from such content.

[@sandysam2164]

Thanks, Bro ... for clarifying the concept of Standardscaling and MinMax Scaling ..... :-)

[@UnfoldDataScience]

Thanks Sandy.

[@engenhariaquimica6590]

Awesome content! Thanks! Keep on

[@UnfoldDataScience]

Thanks for motivating me through comment.

[@rameshwarsingh5859]

SUPERB,explanation,hi

[@UnfoldDataScience]

All the best

[@rameshwarsingh5859]

@@UnfoldDataScience thank you sir 😇

[@BravePrune]

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[@prajwalsyallur712]

Please, make a video on Standard Scalar and Min Max Scalar also.

[@UnfoldDataScience]

Sure Prajwal.

[@firstkaransingh]

Excellent explanation 👌 thank you.
PCA comes under bucket 1 as it uses standardization ?

[@prajwalsyallur712]

Is Feature Scaling apply on one single column or 2 columns combined? From your explanation, it seems like it will be applied on 2 columns combined.

[@rameshwarsingh5859]

supposedly , yes ,but in the real world there are thousands of columns and do feature engineering for take out important columns according to the precisiion of your knowledge and you can apply most used one std..it will distribute all the data points according to the graph

[@asitnayak636]

You told Normalization will get affected by outliers because in its formula we have the maximum no. . But in standardization, we use mean ... Which get easily affected by outliers. So why standardization is not affected by outliers ?

[@deepakts8941]

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[@NikhilKumar-ed2ci]

Bhai sir personal guidance dete?

[@deepakts8941]

@@NikhilKumar-ed2ci I don’t know about that. If he can personally monitor I will definitely take that thing.

[@UnfoldDataScience]

Hi Deepak and Nikhil, kindly reach me on LinkedIN

[@NikhilKumar-ed2ci]

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[@NikhilKumar-ed2ci]

@@deepakts8941 Deepak ji aap apna contact number de sakte?

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Ashish chanchalani lite

[@NikhilKumar-ed2ci]

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[@UnfoldDataScience]

Plz send email with your query. I will try to help.

[@kamaleshkarthi8586]

Dataset description: 4k images with two classes and balanced classes. Using this data set i trained two model using tiny-yolov4.
Model 1 : trained all 4k images. 20k max_batches . getting 84% accuracy avg loss 0.12xxx
Model 2:
Cycle 1 :i trained 3k images with 20k max_batch getting 94% accuracy.
Cycle 2 : i trained 1k images with 20k max batch using last weight of cycle 1. After completion i am getting 94% accuracy and avg loss 0.0xx.
Even though i increased model: 1 20k+20k max batch thare is no improvement.
My question is for both the model i trained with same dataset. why result is different.
Training small dataset is good?
Note: cfg file are same for both model.
Computer configuration are same and gpu resources also same for both the model.
Can you justify it... please
Thanks.

[@NikhilKumar-ed2ci]

Namaste sir
I want some personal guidance regarding data science.I want paid guidance from you.Plz guide me sir for which I shall be ever obliged and grateful to you
Aapse call pe baat krna chahte sir

[@NikhilKumar-ed2ci]

Plz reply sir

[@UnfoldDataScience]

Nikhil, message me on LinkedIn or mail me

[@NikhilKumar-ed2ci]

@@UnfoldDataScience sir what is mail id?

[@NikhilKumar-ed2ci]

@@UnfoldDataScience sir linkedin pe msg send nhi ho raha.Plz reply sir

[@NikhilKumar-ed2ci]

@@UnfoldDataScience sir form bhi fill kr diya Plz mujhse baat kr lijiye