Feature Engineering Techniques For Machine Learning in Python

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Timeline:
00:00 Introduction
1:44 Initial Setup
10:00 Dimensionality Reduction (PCA)
16:22 Preprocessing / Scaling
26:08 Categorical Encoding (Dummy / One-Hot)
33:09 Binning (Grouping / Aggregating)
37:56 Clustering (K-Means)
44:08 Feature Selection

[roblee]

Run a heat map for all columns when viewing correlations before running PCA, there are way more opportunities for dimensionality reduction.

[e_hossam]

for info: if you delete the column island then you should delete the rows containing value 1 as well or you will have the other encoded columns equals zero in all.

[nokroman]

i've been lost in feature engineering chapter on the book that i am currently ready for machine learning right now, and straight ahead i found your video, and with the whole 47 minutes i have learned 2-3 things from you and i understand the whole process lot more better now, this all thanks to you Greg! keep up making these types of videos bcs WE NEED YOU!!

[arsheyajain]

Been waiting for this one!! Amazing video! Thanks Greg

[Eizengoldt]

Greg, your videos are absolutely lovely and reinforce everything I’m learning in my classes, thank you so much

[mikekertser]

I've waited for this video! Many Thanks! :)

[crepantherx]

Buddy, I have subscribed you. Please keep uploading more video that helps a lot

[jacobsquires]

You explain everything in such an easy-to-follow way! Thanks for the amazing video!

[findoc]

this kind of way is what we need outside the uni class. Enough for PCA knowledge in Uni, let's code!

[ShadowDC]

really great comprehensive video, would be great if you did one on how to select features to get the best results for this problem itself

[ashleyb]

Amazing video as always! Super helpful

[hsoley]

Best of the best!
Thank you greg, you are the man!

[rafik]

Thank you a lot for these helpful experiments .. it gave me a lot of ideas in data preprocessing !

[ShaneZarechian]

at 4:30 after looking it up, "frac=1" sets the fraction of the dataset to shuffle to be 100% of the total dataset, instead of say 25%. didn't really explain that properly

[darshanprabhu]

Thanks a lot greg, you have helped me a lot through this videos.

[russelllapua]

Wouldn't this be called Transformation techniques of preprocessing instead? I thought Dimensionality Reduction would be separate from Feature Engineering, with Feature Scaling making up the 3rd subtopic. So something like:

Dimensionality Reduction (removing features)
PCA
Clustering

Feature Engineering (creating/transforming features)
One-Hot
Binning

Feature Scaling of features)
Your scaling

[dvcdbio]

Thank you so much. Your efforts are really appreciated.

[gpsvlogs]

You're Gold !! Keep up the good work.

[kashishrajput]

Thank you for posting this, i like all of your videos :)

[prasadchavan]

great🙌🏻, very helpful keep making more such videos