Discussing All The Types Of Feature Transformation In Machine Learning

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[SALESENGLISH]

Pray your team members recover quickly. India needs good teachers.

[teegnas]

a very important video to review all feature important techniques at one go ... thanks for uploading!

[bhargavikoti]

As usual neatly explained..👍👍thank you for uploading 🙏

[mostafakhazaeipanah]

What A Useful and Informative Video.
Most of the ML Courses are based on Algorithms which they forget the importance of Data Preparation

[giandenorte]

I am looking for these master krish! Take care too

[shivaragiman]

Get well soon, you people need more to us 👍👍👍👍👍

[captainmustard]

thank you sir, it is just an amazing video!!

[pseudounknow]

Greetings from Poland <3 Stay strong India you will overcome this ;)

[nagrajkaranth]

Sir sudhanshu sir tested positive my god please I hope he get well soon

[tanujajoshi]

Hey Krish, Can you explain Generative Adversarial Networks (GANs) especially the coding part for a dataset other than an image dataset?? It would be of great help.

[dheerendrasinghbhadauria]

krish bhai....please upload a PDF of notes of video summary.... along with each video...

[ajaykushwaha-jemw]

Hi Krish, while transformation why we are not dividing our data in Train and Test ?

[ayushsingh-qnsb]

If I have applied some encoding technique, do I have to scale them ?

[poojapatil]

I have completed my 1-year post-graduation program in data science from a leading institute, but the various techniques I learned from your videos in free, were not even mentioned in the curriculum.

Thank you for your easy and detailed explanation.

[alihaiderabdi]

praying for employees of ineuron, inshallah everyone will get well soon.

[yashpandey]

Sir weather scalling is required after performing log transformation ??

[SomeoneElsesSomeoneElse]

With respect to StandardScaler() If you split the dataset prior to scaling the features then don't you risk having skewed features? Put differently, if you train your model to learn that values of 1 get a certain weight and in your test set the data isn't standardized around the same mean as the train set then the model will invariably have worse accuracy unless the train set and test set features have the same mean, right? Shouldn't the test set samples of the full dataset removed only to serve as an "out-of-sample" test? Not two separate datasets?

[priyayadav]

In transformation we transform distribution in Normal distribution.then after transformation we also need to perform Standardisation(Scale down).please tell me if I am wrong.

[mdadilhussain]

I guess that you should first do fit_transform then train_test_split;
As if you have first splited then according to train data you have calculated mean.
Then applies same mean for test data, so test data won't have mean as zero.
Please clear this doubt.

[abhishek_dataman]

Do we require to check this transformation techniques in all binary classification problems?!