Machine Learning Pipeline In Python | How to run pipeline in python machine learning

Machine Learning Pipeline In Python | How to run pipeline in python machine learning
#MachineLearningPipelineInPython #UnfoldDataScience

Hello All,
My name is Aman and I am a Data Scientist.

About this video:
In this video, I talk about step by step process of implementing machine learning pipeline in python. I talk about how to use sklearn pipeline module to implement machine learning pipeline in python. Below questions are discussed in this video:
1. Machine learning Pipeline in python
2. How to run pipeline in python machine learning
3. How to use sklearn pipeline in python
4. Python machine learning pipeline
5. Machine learning pipeline tutorial

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[froilanemeliano]

U definitely know what beginners want to see. Thank you so much for this sir. <3

[MissWhite]

I have been looking for this in a point to point explanation that was done by you in an approximately 10 mins videos that others are taking hours to explain. Very impressive and informative. Please keep up the good work. This going to be very helpful to all the DS Aspirants like me! 🔥🔥🔥

[johnspivack]

Thank you. It's a good video and you try to be helpful. I am still puzzled by how the output from one step in the pipeline gets inputted into the next step. The names of the outputs and inputs aren't explicitly written, so how do you specify or make sure that the right elements of the output will be used the right way and interpreted correctly as the input of the next step?

[dukefler]

Great video, Aman... Simply Amazing... Thanks a lot...

[Ganesh-zjqp]

Hi bro thank you for giving greate information please make video on streamlit

[Parkersneighboursneighbour]

Thank you my dude, you save my life. i dont even why i have to pay tuition to my prof when he fucking shit at his job

[souravbiswas]

Excellent 👌. Without doing the classifier=i, can I do . format(pipelinedict(i))] at cell no 41?

[shivamdwivedi]

Please make vedios on how to choose right algorithms on real world problems .

[abhishekgaurav]

but sir list' object has no attribute 'fit' how your compiler is doing so

[krishnendubhowmick]

So, In this pipeline where to fit data cleansing code ...?

[dees]

Great video. What about imbalanced dataset? what transformer do you use?

[magdynasr]

thanks a lot for this informative video!

[lakshmivagadargi]

sir, can you please make videos on kedros framework?

[mehediazad]

thanks for making it simple to understanding this concept.

[shadow]

This is indeed easy to debug and process. But may I know when do we preprocess the data before splitting and vice versa?

[vishwalamallikarjun]

Hello Aman, Great info .Thanks . I wanted to know if we have to encode the test data (Fit_transform is applied on train data and transform to be applied on test data) how to add these steps in pipeline, i have to use it to deploy the model

[rohanmalik]

But i don't see any valid advantage of using a pipeline over our traditional coding of each models seperately..🤔 (sorry, I'm a beginner in machine learning).
I have already seperately made files for these models with data preprocessing file which contains all possible steps properly highlighted using titles and subtitles in ipynb files.
So I just need to copy paste everytime I get a dataset from these pre-existing code templates. I think the method I use is a very convenient one..🤷‍♂️

[sameertemkar]

Hi Aman, One question..When the X test, and Y test will go thru preprocessing steps (minmaxscaler and pca) as we are only using model.score(x_test, y_test) ..but what about the preprocessing for them?

[bushrajaveria]

hi. great tutorial. could you please guide on how to measure standard deviation using pipelines just like accuracy? Thanks

[ajaykushwaha-jemw]

HI Aman,
I have a doubt.
If we dont user pipeline then we do train test split and prepare same kind of data on X_train and X_test.
Suppose if we have created a pipeline for missing value imputation --> onehot encoding -->scaling the feature then we need to apply pipeline on train and test data ?