Feature Engineering Techniques For Machine Learning in Python
Thank you for watching the video! Here is the Colab Notebook: colab.research.google.com/dri...
California Housing Kaggle Dataset: www.kaggle.com/camnugent/cali...
Learn Python, SQL, & Data Science for free at mlnow.ai/ :)
Subscribe if you enjoyed the video!
Best Courses for Analytics:
---------------------------------------------------------------------------------------------------------
+ IBM Data Science (Python): bit.ly/3Rn00ZA
+ Google Analytics (R): bit.ly/3cPikLQ
+ SQL Basics: bit.ly/3Bd9nFu
Best Courses for Programming:
---------------------------------------------------------------------------------------------------------
+ Data Science in R: bit.ly/3RhvfFp
+ Python for Everybody: bit.ly/3ARQ1Ei
+ Data Structures & Algorithms: bit.ly/3CYR6wR
Best Courses for Machine Learning:
---------------------------------------------------------------------------------------------------------
+ Math Prerequisites: bit.ly/3ASUtTi
+ Machine Learning: bit.ly/3d1QATT
+ Deep Learning: bit.ly/3KPfint
+ ML Ops: bit.ly/3AWRrxE
Best Courses for Statistics:
---------------------------------------------------------------------------------------------------------
+ Introduction to Statistics: bit.ly/3QkEgvM
+ Statistics with Python: bit.ly/3BfwejF
+ Statistics with R: bit.ly/3QkicBJ
Best Courses for Big Data:
---------------------------------------------------------------------------------------------------------
+ Google Cloud Data Engineering: bit.ly/3RjHJw6
+ AWS Data Science: bit.ly/3TKnoBS
+ Big Data Specialization: bit.ly/3ANqSut
More Courses:
---------------------------------------------------------------------------------------------------------
+ Tableau: bit.ly/3q966AN
+ Excel: bit.ly/3RBxind
+ Computer Vision: bit.ly/3esxVS5
+ Natural Language Processing: bit.ly/3edXAgW
+ IBM Dev Ops: bit.ly/3RlVKt2
+ IBM Full Stack Cloud: bit.ly/3x0pOm6
+ Object Oriented Programming (Java): bit.ly/3Bfjn0K
+ TensorFlow Advanced Techniques: bit.ly/3BePQV2
+ TensorFlow Data and Deployment: bit.ly/3BbC5Xb
+ Generative Adversarial Networks / GANs (PyTorch): bit.ly/3RHQiRj
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
Пікірлер: 64
Take my courses at mlnow.ai/!
Been waiting for this one!! Amazing video! Thanks Greg
@GregHogg
2 жыл бұрын
Glad to hear it! No problem 👍😊👍
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.
Run a heat map for all columns when viewing correlations before running PCA, there are way more opportunities for dimensionality reduction.
@GregHogg
2 жыл бұрын
Oh interesting I should look into this
Greg, your videos are absolutely lovely and reinforce everything I’m learning in my classes, thank you so much
@GregHogg
7 ай бұрын
Oh I'm so glad to hear that! Thank you for the support and for the kind words, I really appreciate it :)
Thank you so much. Your efforts are really appreciated.
I've waited for this video! Many Thanks! :)
@GregHogg
2 жыл бұрын
Yup, I know you have been. No problem Mike and thank YOU 😄
Thanks a lot greg , you have helped me a lot through this videos.
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!!
@GregHogg
6 ай бұрын
Awe this is so nice. So glad to have brought a bit of value. Thank you so much for the encouragement and support, it means a lot. Happy learning 😄
You explain everything in such an easy-to-follow way! Thanks for the amazing video!
@GregHogg
6 ай бұрын
Ghost to hear it! Thank you so much, and you're very welcome!
Best of the best! Thank you greg, you are the man!
@GregHogg
2 жыл бұрын
Thanks so much! I really appreciate that.
Amazing video as always! Super helpful
@GregHogg
2 жыл бұрын
Glad you thought so!
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
Buddy, I have subscribed you. Please keep uploading more video that helps a lot
@GregHogg
2 жыл бұрын
Thanks so much, and I absolutely will!
You're Gold !! Keep up the good work.
@GregHogg
2 жыл бұрын
Thanks so much really appreciate that
Thank you a lot for these helpful experiments .. it gave me a lot of ideas in data preprocessing !
@GregHogg
5 ай бұрын
Super glad to hear it!
Thank you for posting this, i like all of your videos :)
@GregHogg
Жыл бұрын
Great to hear!!
great🙌🏻, very helpful keep making more such videos
@GregHogg
Жыл бұрын
Thanks so much, will do!
this kind of way is what we need outside the uni class. Enough for PCA knowledge in Uni, let's code!
This is very informative....
thank you bro
Thanks! Very usefull!
Thanks man
amazing tutorial
great job thank you
@GregHogg
6 ай бұрын
You're very welcome!
thanks!
well explained thanks
@GregHogg
2 жыл бұрын
You're very welcome!
Superb
thank you so much
@GregHogg
2 жыл бұрын
You are very welcome!!
many thanks
@GregHogg
Ай бұрын
Many welcomes!
Love it
tnx
do u have a tutorial on catBoostencoder
Thank you I wish i was as smart as you ughhh but at least i learned some from this
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 (normalisation/standardisation of features) Your scaling
great video. pkease talk slowe for the beginners
@GregHogg
6 ай бұрын
Thank you!
Well explained., but why do you keep turning everything into numpy arrays? I do not think it is necessary.
@GregHogg
23 күн бұрын
I guess it's just a habit... Probably not necessary yeah
Wish I could give more likes!
@GregHogg
Жыл бұрын
One will do ☺️
Great vid, but slow down your speaking so lesser experienced people can follow along!
@GregHogg
2 жыл бұрын
Thank you. If it is too fast, there is a slowdown option :)
@beauclark2199
2 жыл бұрын
@@GregHogg Yes, i can absolutely slow down the speed of the tutorial. Have a great day
@GregHogg
2 жыл бұрын
@@beauclark2199 great! You too!!
Too long and boring this guy is not straighforward
@GregHogg
3 ай бұрын
Ikr Screw this guy