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.

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Thanks and see you in future videos!

#featurescaling #normalization

[samuelkoramoah]

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

[alexismachado]

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.

[bogdancristurean]

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!

[malenawong]

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

[jingyiwang]

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!

[amrittiwary]

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.

[sukhwinder]

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

[vskraiml]

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.

[ykecflv]

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.

[bartekdurczak]

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 <3

[ifeanyiedward]

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.

[beloaded]

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

[lleger]

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

[littlehelper]

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 😊

[lleger]

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

[memories-fn]

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

[Sickkkkiddddd]

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

[_faizalabdillah]

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

[deepakkumar-ejje]

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