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

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!)

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

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

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

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

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

the best video on Standardization & Normalization

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

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

26 күн бұрын

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.

Incredible explanation, thank you very much!

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.

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

@amanpatra8092

2 жыл бұрын

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

Brilliant explanation. Thank you sir!

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

One of the best teachings on this subject. Thanks Krish

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!!

Great explanation. Very well conveyed with proper examples

Thank you! Perfectly explained.

Very informative and helpful, thanks a lot Krish

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

@deeptipancholi8814

3 ай бұрын

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

@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

Thanks for the intuitive explaining.

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

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

Thanks for your suggestion 🙏

So clear explanation, thanks Krish

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

Awesome sir, you are explaining very easy way .

best explanation, keep it up

You're a great teacher. Thank you.

wow! great explanation .. Thank you 🙏

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

Great explanation !! Thanks

Great Job very well explained

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.

Great Explanation, Thank you!

Thanks for the awesome explanation

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

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

thank you so much you are the man !!

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?

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?

Thank you. Great explanation.

Nice and useful information 👍

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.

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.

Absolutely excellent explanation

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!

Thank you so much !!!

Thank you sir..nice explanation :)

always the best explanation!

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

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

Great explanation!

Excellent explanation!

Thanks a lot Krish :)

Thank you this was very helpful.

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

I love you mate, thx Cheers!

Very informative thank u

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.

Great explanation Sir

super guru! U made it a cake walk

Paji tusi great ho :)

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

2 жыл бұрын

Thanks

Great explanation

Excellent!!

Good explanation!!!

Thanks Krish

Clean explanation Thanks

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

thanks for your excellent explanation, but it confuses me when I try to filter lower-variance features, standardization isn't suitable because it scales all features' variances to 1, so in this case I should try normalization, but then is it right to perform PCR or PLSR next?

Thank you sir ♥️

Hi Krish.. I recently use standardization in classifier model and it improve my accury. I am aware that for classification method standardization is not recommended but in my case its improving accury. What would u suggest?

Thanks sir for explanation

Hello Sir, Generally the range for normaization is 0 to 1. But I have read papers where different ranges are used Say 1 to 7. I want to know the logic/criteria behind selecting the target range?

@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.

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

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

So superb.

Hi, Krish. For instance, I train my model on normalized dataset and I need to use this model. I have to classify ONLY ONE test object. How should I normalize this object?

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

3 жыл бұрын

can u please explain ur outliers point

@lars1597

3 жыл бұрын

@@crackthecode1372 outliers are just noise

@TheMaverickanupam

3 жыл бұрын

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

@bhaskartripathi

2 жыл бұрын

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

I like your why of explaination

Your videos are 🔥🔥

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

KZread hid your Videos from My Feed Bro!!! Thanks for the Explaination!!!

very good explanation

Thank you so much bro

Hi Krish, What will happen to notmalization if outliers are present in the data? Outlier treatment is necessary before applying notmalization? There a method in sklearn normalize, will it same as minmaxnormalizer.

after fitting model and predicting values using normalised/ standardised data, how to get back the original values for predicted results. no where reversing of normalisation is shown??? have had any video on this?

Thank you for the video. It was useful. Can you please provide the github link?

hello krish .awesome video .But lease provide the GitHub link for practice.I have searched over in GitHub profile of yours but could not find it

thanks so much

Brilliant!

Thank you

I liked this particular video

Very nice video

Thanks a lot

Help me a lot!