Normalization Vs. Standardization (Feature Scaling in Machine Learning)

[@samuelkoramoah3552]

this is by far the best explanation I've come across. So simple to understand. Thank you Prof. You just earned a follower!!

[@twanwolthaus]

Your explanation is as amazing as a rainbow cloud after a thunderstorm!!! I'm so glad I found this visual explanation!

[@bogdancristurean73]

This was pretty clearly explained. For anyone else looking for this, the standardization chapter begins at 6:49.

[@CatanTech]

2 mins deep and I have the concept already... Great Job Professor.

[@Mai.Data123]

Thank you so much for this. For a non-IT person trying to learn Python, I understood this finally!

[@MayurSunkersett]

This is my first time that I am watching your video.. You look very ..very much similar to Saif Ali Khan.. In fact the smile is also same. One like vote from me. A gentle smile on face make you different from all the others.

[@alexismachado2262]

Great explanation however i think saying scaling is not required for distance based algorithm is wrong as these algorithm are most affected by the range of features. Can you comment on this.

[@rafaelposadas2341]

I think the same

[@shahzarhusain3662]

Exactly! Scaling is crucial for distance based algorithm.

[@bernardesp_]

I believe that such as in the case of k-means, the algorithm calculates distances based on column versus same column as opposed to a neural network were each column can have a impact on target output. As distances are measured in the same scale (column x column), of course one feature is going to affect more clusterization {for instance}, but that's the point of k-means, we want to see which features describe data distribution across dimensions.

[@owdengodson2990]

Yeah, this is where I also paused and commented as well!!

[@hcalbukaj]

facts

[@jingyiwang5113]

I am really grateful for your detailed explanation! I am self studying machine learning this summer holiday. And I am at this point now. I am so confused before watching your video. Now I finally understand this point. Thank you so much!

[@leixiao169]

Thank you for the clear explanation!

[@malenawong167]

Thank you so much! You've explained this so clearly! I'm very new to ML and this has helped me so much!

[@xXMo7aLXx]

جزاك الله خير بروف ريان, شرحك جدًا ممتع وواضح وطريقة تفسيرك للخطوات شيء هائل. شكرًا لك

[@lleger]

the outlier thing is so crucial actually damn, i havent seen this is in a machine learning course before, banger

[@amrittiwary080689]

Great video, would say we need scaling for distance-based as it will get wrong results if features are on different scales. We don't need scaling for tree-based as they are not susceptible to variance.

[@vskraiml2032]

Impressed with your way of teaching. You are explaining very well with the right examples... awesome work of you... One small request is that in your playlist sequence of 'Artificial Intelligence, Machine Learning, and Deep Learning' is jumbled, please keep the playlist in order for easy learning.

[@bartekdurczak4085]

King !!! very good explantation. I watched multiple videos on yt and i asked Chatgpt many questions but now after your video i finally understand it

[@1littlehelper]

Hi Professor, thank you so much for this video! Clear and concise you have no idea how much I needed this. Keep up the great work, I will be sure to check out your other videos as well 😊

[@SaFFire123x]

Just came from a KMeans clustering course that demonstrates how normalization results in better clusters. But at 11:40, you say KMeans clustering doesn't require standardization or normalization. I'm confused.

[@sukhwinder101]

For ML context : if data is following gaussian distribution ( bell shape) follow standard deviation else go with normalisation ( improves cluster scaling as well).

[@Sickkkkiddddd]

Came here from your udemy course. You are a life saver, prof!

[@lleger]

thank you boss man, just used normalization instead of standardization, life saver

[@beloaded3736]

This professor is so pleasant for all senses. Thanks for sharing knowledge selflessly :)

[@ifeanyiedward2789]

Thank you so much Professor Ryan. You just made my life easy. best explanation. so simple to understand even for someone who doesnt have a background knowledge in machine learning.

[@memories-f3n]

Well explained about standardization and normalization.Now i got full clarity on these topics.Thanks for taking this effort and explaining in this way.

[@deepakkumar-ej1je]

Hello Professor, Video was able to explain the concepts and its practical implementation in a concise manner. Awesome work

[@professor-ryanahmed]

Many thanks!

[@57_faizalabdillah99]

Amazing Explanation.. Just in one run, i get your whole point in an easy way. Big Thanks

[@TheOraware]

At 11:27 you mentioned in last bullets that scaling is not required for K-NN and SVM is not correct. K-NN and SVM exploits distances or similarities they do require scaling.

[@floriant9104]

very true!!

[@yasmineelezaby5197]

Thank you so much! I couldn't wait to end this video before thanking you ! you made it super clear.

[@PJ-od9ev]

A great scientist and teacher. keep it up, sir. thank you.

[@catulopsae]

Awesome. I understand finally. Very good explanation. Easy to follow

[@nutanaigal9761]

thanks a lot ...worth watching..u explanined each concept in a simple way...

[@yamanarslanca8325]

11:40 wait I am confused now, because I thought that since the distance of the data is so important in algorithms such as kNN, SVM etc. scaling is a MUST pre-process step, but now you are saying that it is not required ? Could you please clarify this ?

[@dunwally2433]

Can you share the dataset you used for this demo pls?

[@albertoavendano7196]

Many thanks for this video... One of the best explanations ever seen by me

[@zanyatta1]

The best simple explanation ever

[@Alias.Nicht.Verfügbar]

the best explanation! finally understood, thanks!!

[@plowface]

I'm finding a lot of sources are saying feature scaling is advised when using k nearest neighbours. Is there more nuance to this point? Is scaling required after all?

[@lethalgaming7087]

Thank You Leonard Hofstadder..🙂

[@professor-ryanahmed]

Hahaha thanks ❤️😂

[@muhammadabdurrazaq2069]

Thank you for your best explanation as easy to understand

[@owdengodson2990]

What happens when we have features like age (min - 20, max - 60) and salary (min - 40,000, max - 300,0000). Won't our KNN algorithm cause all distance based models to be biased and give more weight to features with higher values? Salary in this case. Won't this effect be eliminated by scaling the features?

[@leu2304]

Thank you so much. Excellent explanation!

[@atharvambokar573]

This was such a crystal clear explanation! Thank you so much sir!

[@yosefasefaw4207]

amazing video! clearly explained! Congratulation Professor !

[@mahamadounouridinemamoudou9875]

thank you very much, I can't pass without thanking you and subscribe for the clarity you gave me on that topic

[@ARCsGARDEN]

Can you please share the github repo link for accessing the data files used in the video

[@ArvindKumar-vr4gf]

How to apply z score normalisation in live data ??? 🙏🙏🙏

[@caliguy1260]

Awesome explanation for a beginner like me. Wish I had access to the S&P 500 dataset.

[@andyh3970]

could you put a link to the csv file so we can download and try the exercise ourselves please?

[@vijayarana2087]

Many thanks for this video... One of the best explanations

[@asyakatanani8181]

as always: outstanding! Your enthusiasm is inspiring... On the other hand, it is clear why tree-based algorithms do not require feature scaling. However, distance-based algorithms such as K nearest Neighbors and K-means require Euclidean Distance calculation which means that feature scaling is necessary with them. Am I wrong?

[@whynot13]

I think you should scale features for K-means and K-nn. Think about it intuitively. If you are looking at two points and their x y (feature) distances, how would you want to define their closeness? Do you want their features to be considered equally when calculating your distance or is one feature more important then the other ? If you want both x and y to be considered on equal playing fields, then you should scale them so that the distance computed reflects their importance. Scale each feature by the method that makes more since to that feature. This is most likely [0 to 1] across samples.

[@zaldi19]

Question, what if our model encounters bigger value than what we had in training data? How do we handle that

[@patientmuke7008]

For supervised algorithms, can we used both as data input ?

[@tamerelkot7807]

how can i download the csv file of the data u have used

[@believer8754]

top explanation along with code, can you upload the notebook file with each video u explain . thanks

[@muralidhargrao]

Hi Prof. Ryan, Thank you for explaining the subject in a simple manner. I have a Human Resources situation at hand. We have an employee appraisal system and the rating is on a 6 point scale (ranging from Poor performer to Outstanding performer). We have 15 departmental heads who rate their respective team members on this 6 point rating scale. However, there are immense biases that creep in during evaluation. Also, some evaluators are tougher/lenient than others. Consequently, we end up with different ranges/averages. As the ratings are linked to incentives, sometimes, good performers lose out against their peers in other departments. I intend to eliminate this bias/lack of neutrality which have been rated by 15 different departments (for 1000 employees). Can you suggest how I should go about this situation please. Regards...Muralidhar

[@odosmatthews664]

Can you show an example of scaling with train test split? Do you scale the train and test data with the same scaler?

[@joguns8257]

Please, where's the link to the dataset? I'd really appreciate if you can paste it here, Prof. Thanks a lot.

[@noonereally0007]

hey professor, that was a very cool and simple video to follow and understand, could i ask for where i cold find the notebook you used at the end to use?

[@dianavi3961]

Thank you! Everything is clear now

[@fiqrifirdaus]

clear as a crystal, thankyou

[@tasnimsart3430]

Such a great explanation. Thank you very much

[@sm-pz8er]

Perfect explanation. Thanks

[@harshvardhanbhanot8736]

Thanks Prof, exactly what I needed

[@AbrahamStrange-tt4fv]

Great explanation. Thank you very much, Sir!

[@ravikumaryalangi7100]

Amazing explanation..

[@joguns8257]

Superb illustration.

[@professor-ryanahmed]

Thank you so much 😀

[@joguns8257]

@@professor-ryanahmed You're welcome, Prof. Please, the link to the dataset?

[@AndromedHH]

Fantastic explanation ! Thank you so much.

[@anuradhabalasubramanian9845]

Fantastic Explanation Sir ! Thanks so much !

[@anp9929]

you've not missed a single base brother. what an explain

[@jyothsnaraajjj]

Excellent explanation.

[@lucasgonzalezsonnenberg3204]

Firstly, I like very much your explination. Secondly, I would like to know, how do you plot the row and rescalled data? Do you use the histograms function from pandas? Thank you very much and keep working so on!

[@lucasgonzalezsonnenberg3204]

I have all ready founded. :D import seaborn as sns sns.pairplot(df)

[@Gebev]

Outstanding content.

[@NickMaverick4]

Good theoretical explanation.. but I think scaling is used for k means, knn

[@amirshahmie]

You're the best prof!

[@shadyshawky6737]

Very Clear Explanation. Thank you :)

[@chandrasekharnettem1537]

distance-based methods assume that features are normalized?. feature scaling is required?. please confirm that?. tree-based does not need scaling

[@algosavage7057]

good. clearly explained. thanks

[@MDMushu-ff5od]

really really amazing sir

[@jiberuba8856]

Thank you. Where I can download the notebook code?

[@ShawnBecker11]

I also have this question

[@Diamond34qw]

Thank you so much, Prof!

[@KarinaRodriguez-tb6ol]

Amazing explanation!

[@arjundev4908]

He used to be on Stemplicity as well.

[@remmaria]

Great explanation!! Could you say more about when the input is image datasets - like CNNs?

[@apratimmehta1828]

I also need the answer . On which axis scaling is to be done

[@mohammedobad2174]

I think distance based algorithms required scaling. Please double check

[@gaberhassan3972]

Great job 👏👏❤

[@MUHAMMADAHMAD-c7m]

Informative!

[@MariaDonayreJackson]

Excellent thanks!!!

[@mamounarakza5951]

حبيبي يا بروف

[@sibeltoprakkiran3758]

I read in different sources that KNN needs scaling. But you say, that it doesn't.

[@cvino0618]

Could've added this into your udemy course

[@saremish]

Excellent!

[@FRANKWHITE1996]

Thanks for sharing ❤

[@alhelalyhossam]

I really liked your explanation, thanks P.S. Are you Egyptian? I mean your accent is perfect, but your pauses while speaking give the intuition that you're from the Great Egypt.

[@rohithgowdax]

It's was really helpful ❤

[@sanumioluwafemi7247]

Thank you for this video

[@professor-ryanahmed]

My pleasure

[@jimherebarbershop8188]

Gr8 explanation!!!

[@sololife9403]

Thank you Prof!

[@yossryasser2646]

where can I get the dataset?

[@things-tz8dj]

dataset please

[@4abdoulaye]

Appreciated it, Thanks.

[@poizn5851]

Thank you it is helpful