Normalization Vs. Standardization (Feature Scaling in Machine Learning)

In this video, we will cover the difference between normalization and standardization.
Feature Scaling is an important step to take prior to training of machine learning models to ensure that features are within the same scale.
Normalization is conducted to make feature values range from 0 to 1.
Standardization is conducted to transform the data to have a mean of zero and standard deviation of 1.
Standardization is also known as Z-score normalization in which properties will have the behavior of a standard normal distribution.
Check top-rated Udemy courses below:
10 days of No Code AI Bootcamp
www.udemy.com/course/10-code-...
Modern Artificial Intelligence with Zero Coding
www.udemy.com/course/modern-a...
Python & Machine Learning for Financial Analysis
www.udemy.com/course/ml-and-p...
Modern Artificial Intelligence Masterclass: Build 6 Projects
www.udemy.com/course/modern-a...
AWS SageMaker Practical for Beginners | Build 6 Projects
www.udemy.com/course/practica...
Data Science for Business | 6 Real-world Case Studies
www.udemy.com/course/data-sci...
AWS Machine Learning Certification Exam | Complete Guide
www.udemy.com/course/amazon-w...
TensorFlow 2.0 Practical
www.udemy.com/course/tensorfl...
TensorFlow 2.0 Practical Advanced
www.udemy.com/course/tensorfl...
Machine Learning Regression Masterclass in Python
www.udemy.com/course/machine-...
Machine Learning Practical Workout | 8 Real-World Projects
www.udemy.com/course/deep-lea...
Machine Learning Classification Bootcamp in Python
www.udemy.com/course/machine-...
MATLAB/SIMULINK Bible|Go From Zero to Hero!
www.udemy.com/course/matlabsi...
Python 3 Programming: Beginner to Pro Masterclass
www.udemy.com/course/python-3...
Autonomous Cars: Deep Learning and Computer Vision in Python
www.udemy.com/course/autonomo...
Control Systems Made Simple | Beginner's Guide
www.udemy.com/course/control-...
Artificial Intelligence in Arabicالذكاء الصناعي مبتدئ لمحترف
www.udemy.com/course/artifici...
The Complete MATLAB Computer Programming Bootcamp
www.udemy.com/course/the-comp...
Thanks and see you in future videos!
#featurescaling #normalization

[@samuelkoramoah3552]

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

[@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!

[@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.

[@bogdancristurean73]

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

[@twanwolthaus]

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

[@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 😊

[@GreatIndiaTrips]

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

[@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.

[@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.

[@sukhwinder101]

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

[@57_faizalabdillah99]

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

[@beloaded3736]

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

[@Sickkkkiddddd]

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

[@yasmineelezaby5197]

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

[@fiqrifirdaus]

clear as a crystal, thankyou

[@lethalgaming7087]

Thank You Leonard Hofstadder..🙂

[@professor-ryanahmed]

Hahaha thanks ❤️😂

[@atharvambokar573]

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

[@yosefasefaw4207]

amazing video! clearly explained! Congratulation Professor !

[@PJ-od9ev]

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

[@albertoavendano7196]

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

[@anuradhabalasubramanian9845]

Fantastic Explanation Sir ! Thanks so much !

[@nutanaigal9761]

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

[@louisCS502]

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

[@catulopsae]

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

[@AndromedHH]

Fantastic explanation ! Thank you so much.

[@shadyshawky6737]

Very Clear Explanation. Thank you :)

[@AbrahamStrange-tt4fv]

Great explanation. Thank you very much, Sir!

[@user-yk3ec4fl5v]

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.

[@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.

[@sanjeevjangra84]

Awesome explanation. Thank you!

[@vijayarana2087]

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

[@tasnimsart3430]

Such a great explanation. Thank you very much

[@zanyatta1]

The best simple explanation ever

[@algosavage7057]

good. clearly explained. thanks

[@louisCS502]

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

[@mahamadounouridinemamoudou9875]

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

[@leixiao169]

Thank you for the clear explanation!

[@muhammadabdurrazaq2069]

Thank you for your best explanation as easy to understand

[@KarinaRodriguez-tb6ol]

Amazing explanation!

[@jyothsnaraajjj]

Excellent explanation.

[@ItsTheGameDude]

Thank you so much, Prof!

[@EvaPev]

Outstanding content.

[@MariaDonayreJackson]

Excellent thanks!!!

[@user-zb5zi3ll3g]

Informative!

[@4abdoulaye]

Appreciated it, Thanks.

[@saremish]

Excellent!

[@jimherebarbershop8188]

Gr8 explanation!!!

[@remmaria]

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

[@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!

[@caliguy1260]

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

[@sololife9403]

Thank you Prof!

[@poizn5851]

Thank you it is helpful

[@FRANKWHITE1996]

Thanks for sharing ❤

[@gaberhassan3972]

Great job 👏👏❤

[@sanumioluwafemi7247]

Thank you for this video

[@professor-ryanahmed]

My pleasure

[@joguns8257]

Superb illustration.

[@professor-ryanahmed]

Thank you so much 😀

[@joguns8257]

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

[@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?

[@peaceadesina]

Thank you!

[@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

[@NickMaverick4]

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

[@zahra-pl1sk]

SUPEEEEEEEER clair. thanks

[@odosmatthews664]

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

[@dunwally2433]

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

[@samws_4]

Helpful!

[@mamounarakza5951]

حبيبي يا بروف

[@patientmuke7008]

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

[@hasszhao]

thx Prof

[@user-le2cc1yt8n]

رائع .. متميز

[@anp9929]

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

[@arjundev4908]

He used to be on Stemplicity as well.

[@zaldi19]

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

[@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.

[@believer8754]

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

[@user-lk1fd7lz3c]

thx

[@liviumircea6905]

Good

[@amineazemour2476]

top of the top

[@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)

[@mahmodi5timetolearn]

The best, marhaba

[@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!!

[@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?

[@joguns8257]

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

[@ArvindKumar-vr4gf]

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

[@andyh3970]

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

[@ARCsGARDEN]

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

[@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.

[@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 ?

[@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.

[@yossryasser2646]

where can I get the dataset?

[@jiberuba8856]

Thank you. Where I can download the notebook code?

[@ShawnBecker11]

I also have this question

[@cvino0618]

Could've added this into your udemy course

[@jeffkamuthu3276]

A whole semester in 20 minutes

[@chandrasekharnettem1537]

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

[@things-tz8dj]

dataset please

[@mariwanahmad9362]

Dear Rayan, how to test a scaled data model. i used this way the predict value is very different X_testing=np.array([[550,440,110,0,0,0,0.33,400,8.8,0,863,771]]) #78.6 ypred=model.predict(scaler.fit_transform( X_testing)) # predicted should be 78 , but i got [[0.17291696]] also without fit_transform also the value is different. many thanks for you replay.