Standardization Vs Normalization- Feature Scaling

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[@srikrithibhat6220]

One of the best and detailed explanation on Scaling Techniques. Thank You so much Krish ji.

[@tymothylim6550]

Thank you Krish for this video! It was fantastic in helping me understand the difference between these 2 things and some additional advice regarding how it helps with some other things (e.g. helping some kinds of models optimize faster!)

[@rohitkamra1628]

I have completed Statistics Playlist. You explained in a very good way. Thanks for this. :)

[@raziekhairy5799]

Thank you. I wish this world will be fulled of people like you!

[@sagaryadav3473]

I'm really in love with the way you explain. So nice :)

[@gc-0377]

I love you dude, thanks for the explaining you saved me, greetings from México wey you have a new sub

[@vaibhavnakrani2983]

You explain it very simply. I love it. I even even recommend your videos to other guyz in ML.

[@baharehghanbarikondori1965]

the best video on Standardization & Normalization

[@GuitarreroDaniel]

Incredible explanation, thank you very much!

[@momaalim3086]

Brilliant explanation. Thank you sir!

[@donaldngwira]

One of the best teachings on this subject. Thanks Krish

[@aditisrivastava7079]

Thanks for your suggestion 🙏

[@varshapatil]

Great explanation. Very well conveyed with proper examples

[@najmeh5707]

Thanks for the intuitive explaining.

[@dungtran-vk3ed]

Here you go. Hope it can help you guys
df = pd.read_csv('raw.githubusercontent.com/rasbt/pattern_classification/master/data/wine_data.csv', header= None, usecols=[0,1,2])

[@maruthiprasad8184]

Thanks

[@nonamenoname1942]

Thank you! Perfectly explained.

[@anujashinde5717]

Hi sir. I have seen lots of videos on machine learning but I personally feel like u r d only one who’s making the videos in very fantastic way. u explains all the things in such a way that even the person who is from non technical background can understand it. Just a small req for you. Can u pls make video on all the techniques that can be apply on single data set. Like when to scale the data & apply PCA, clusters, algorithms, when to do label encoding instead of one hot. Can u pls apply all these things on any dataset so that i can have clear insight on model building. Can u pls make video on this for end to end model building

[@nakul469]

Hi, I just read your comment and I wanted to know how's your data science career going? I just completed ML and going to create an ML project for resume. Can you please give me any kind of suggestion if you are reading this comment.

[@aashishsahni90]

Great way of teaching...really helpful!! :)

[@santoshandawarapu340]

I have gone through other speakers videos but they are hard to follow. I really liked the way of explanation in a very simple way with great examples. Thank you brother.

[@mapy1234]

Great!...very good explanation...plz keep posting...thanks

[@vgaurav3011]

Finally completed your statistics playlist and can definitely say learned much more than other online courses

[@amanpatra8092]

hello , i want to learn statistics for data science i don't have prior knowledge, will this cover the basics as i want to start from scratch

[@user-uz5ld4oi6r]

Clear message, clear structure, easy to understand, thank you

[@AbcAbc-kx3xm]

So clear explanation, thanks Krish

[@user-qz1hd4xp1p]

thank you so much you are the man !!

[@lolwhatyesme651]

You're a great teacher. Thank you.

[@owoeyebabatope2425]

This short video has helped me understand a great deal of feature engineering. God bless you. I wish to learn more from you. I recommend you do a video on a full data science project and focus more on the thought process. While you also do a soft touch on various alternatives to whatever method you have used.
This is Great!

[@ahmeddhiael-euch8105]

Very informative and helpful, thanks a lot Krish

[@anshukaurav2896]

Awesome sir, you are explaining very easy way .

[@akshatabm4491]

Great content. Thank you for explaining in the best way possible. However a small suggestion, please include the links of dataset your are using in the description box. It will be helpful to practice along while watching the video. Thanks again, cheers!!

[@rasha9462]

wow! great explanation .. Thank you 🙏

[@nirmalpatil5370]

Thank you so much !!!

[@idrissjairi]

Great Explanation, Thank you!

[@uchchwasdas2675]

best explanation, keep it up

[@andresherrera4023]

Great explanation !! Thanks

[@sayantansinha4545]

Thanks for the awesome explanation

[@aryanchauhan9086]

I hope one day I will become data scientist like you , you are really helpful for aspiring data scientist like me

[@deeptipancholi8814]

Hey
Have you become data scientist ?
If yes please suggest me something

[@hasibullahaman50]

Thanks to you Chanel...
it's so helpful for my UNI Lesson

[@maximilianovazquez59]

I love you mate, thx
Cheers!

[@murmupk]

Sir, you should also mention the dataset link in the description. This will help us to follow you.

[@user-es3wr6uf2l]

Thank you. Great explanation.

[@alexeivodopianov5440]

Absolutely excellent explanation

[@adhipathis12]

Thanks a lot Krish :)

[@ShahnawazKhan-xy1ll]

Great Job very well explained

[@visualvalidator5384]

Commenting exactly on the same date, such a coincidence though, Thank you Krish for this video!

[@saltanatkhalyk3397]

always the best explanation!

[@keerthanpu808]

super guru! U made it a cake walk

[@vikeshgiri2369]

Today only started this playlist and today only completed, it is possible because the way sir❤️ explain is just amazing..❤️ Now I move to next part.

[@ashimmaity64]

sir, please make video on difference between GD,SGD,SGD (mini batch),SGD with momentum.

[@imamamansoor5174]

Krish whenever i get confused for any Data Science topic, i search it on YT, if your video pops up for it, i definitely select your explanation for that topic.

[@josealjndro]

In most of the cases I reproduce this kind of videos at 1.25x velocity, this one 0.75x haha nice videos Krish!

[@VJ-gj2yl]

Excellent!!

[@abhinavmahajan448]

Paji tusi great ho :)

[@jaypatel2436]

Just woww ❤️

[@bhabanisankardash7387]

Nice and useful information 👍

[@AugustNocturne]

Thank you this was very helpful.

[@belimmohsin]

Thank you sir..nice explanation :)

[@louerleseigneur4532]

Thanks Krish

[@purnimaps9819]

Very informative thank u

[@shadiyapp5552]

Thank you sir ♥️

[@ahmedelsabagh6990]

Great explanation

[@tosinlitics949]

Excellent explanation!

[@princemensah1866]

Great explanation!

[@joguns8257]

So superb.

[@sineadgillespie-mccracken140]

Brilliant!

[@latabisht3591]

Great explanation Sir

[@marcoreiter2795]

Thank you for your video Krish, it was really helpful!

[@mathivathanankunarasa4076]

Thank you so much bro

[@jedidiaholadele2086]

Your videos are 🔥🔥

[@chinmaybhat9636]

@Krish Naik Sir Github Link is not there in the description link, for the Jupyter Notebook shown in this video, Can you Share the Same ??
Thanks & Regards,
CHINMAY N BHAT

[@danielagbaniyaka6196]

Clean explanation Thanks

[@bhavanasaraswat7808]

Thanks sir for explanation

[@obi6753]

thanks so much

[@KrishnaChaitanyakosaraju]

In linear regression, one common assumption is that all the features have 0 mean same variance. Which is similar to standardization. Hence it works.

[@manueldelcristo1]

Good explanation!!!

[@navjiwanhira6175]

Thank you

[@Kim-bn4ub]

HI, can you please add the github link in the description? the github address is missing.

[@umarfadhil]

Help me a lot!

[@swm7f137]

I like your why of explaination

[@vagheeshmk3156]

Krish. You ARE the Guru of DataScience for aspirants stuck in the Dark......
#KingKrish

[@saschageyer1026]

I liked this particular video

[@mirakaddour2753]

u are the best

[@123anandik]

Good one 🤘🤘🤘 Actually z score is much widely used for most of the algorithms as i have seen.
And I do practice the same all the time.
The reason is the affect of the outliers.
Outliers can be easily detected by z score.
Normalistion between 0 to 1 just shrinks curves.

[@crackthecode1372]

can u please explain ur outliers point

[@lars1597]

@@crackthecode1372 outliers are just noise

[@TheMaverickanupam]

@@lars1597 Sometimes outliers are important noise. Outliers can tell a lot about data. They can't simply be dropped.

[@bhaskartripathi]

Minmax scaler is the most widely used in forecasting research papers. Z-score is not very good in time series forecasting

[@SindyanCartoonJordanian]

Thanks a lot

[@ashabhumza3394]

I had been watching all your previous statistics videos and understood each concept well.
Since I am not from mathematics background,
In this video I couldn't understand what you explained in the part while telling what process to use when.
Will this be a matter to bother in my data science learning journey?

[@avinashtandle3728]

very good explanation

[@georgekokkinakis7288]

Have to say that your presentations really stand out , basically because of the distilled informations and to the point suggestions you make. One question though. At some point you talk about CNNs and that we have to use MinMax scaler. I am using CNN but on non image data, basically I see my data as an image of point values. Should I go with MinMax scaler or I could also use Standard scaler? And in order to be more specific lets say that I have an image of 7x7 where I want to keep the relative value differences between a pixel and its neighbours. Which scaling should I use in your opinion? Can we use standarization on the dataset in order to train a CNN or the values should be in [0,1] so we have to use minmax scaler. I am really interested to hear your opinion based on you experience.

[@spiderman8340]

TH-cam hid your Videos from My Feed Bro!!! Thanks for the Explaination!!!

[@kamaladey2442]

Thanks Sir for sharing all wonderful videos, kindly provide the github link to download the dataset ,not getting from description box

[@AlbertRyanstein]

Hi, I really enjoyed the video. I was wondering is this the same as normalisation on keras.

[@Raja-tt4ll]

Very nice video

[@amoldumrewal]

Hi Krish, one quick question. I was going through some tutorials for batch normalization and got confused with which technique is used there. It seem like they first do min-max followed by standardization. Can you please help me here?

[@uniquetobin4real]

Krish u are the best♠️♠️

[@user-gj8ps9hn7h]

thank you that was helpful

[@bonikapp6504]

I'm always humble and greatfull to you.... your teaching is wonderful ❤️..And your selfless attitude and practical approach is truly amazing ...❤️❤️❤️🙏
I just stuck to your Chanel
Thank you so so much...🙏

[@Sarasara-dg8gb]

What is the most adequate way of features scaling for ANFIS algorithm, normalization or standardization?

[@lokeshgaikwad4337]

thanks for the great explanation sir .the link of channel you are talking about is not working pls help with that

[@vishalaaa1]

Good topic

[@nothing8919]

Now I completed the statistics playlist and finally can move to the third part

[@matinpathan5186]

Me too moving for the next Playlist that is Feature Engineering... Good Luck

[@nothing8919]

@@matinpathan5186 Good luck to you too

[@nutsom]

@krish - i didn't quite understand when to use Normalisation and when to use Standard Scaler. Can u share with an example why standard scaler was used and another one where normalizer or min max scaler was used, and why.

[@utkarshpandya3155]

Hi Krishna.You're saviour.Apologies in advance if it is already asked question.(please advise if you have already answered and will find out the video).
1.Do you have any usecase where you do standardisation (with mean & std ) followed by min-max normalization so that you can compare same scalled features and then fit them into 0 to 1 or let's say 0 to 100 or -50 to +50 etc ?
2. any pros and cons of standardisation followed by min-max normalization ?
3. am i missing any logic by asking ? is there any solution for a scanario where you have more than 5 + features and user want it to scale in a single number so that instead of viewing the movement or change of 5 features,you will only focus on final score by means of min-max norm....hope it's clear out my question
looking forward to see your answer.Regards & TIA + Thanks for this video.