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Distributed Machine Learning with Apache Spark / PySpark MLlib

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The Kaggle housing.csv file: www.kaggle.com...
The Colab Notebook: colab.research...
PySpark RDD Introduction: • Apache Spark / PySpark...
PySpark SQL Introduction: • PySpark Tutorial: Spar...
PySpark MLlib Docs: spark.apache.o...
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Опубликовано:

28 авг 2024

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Комментарии : 23

@GregHogg Год назад

Take my courses at mlnow.ai/!

@nickie2222 Год назад

Thank you for the video on mllib, I haven't watch much yet, but looks promising. The machine learning stuff starts about 12:00 - the beginning is a warm up to PySpark. Chapters/timestamps would have been helpful for a 40 min video (with each chapter being a different stage in the process or function).

@erint.4917 Год назад

Great tutorial, Greg - really appreciate how you distilled such a comprehensive overview into a single video. Would you consider doing a video showing how to create a complete ML pipeline -- i.e., using output from Imputer(), StringIndexer(), OneHotEncoderEstimator(), VectorAssembler(), and VectorIndexer() -- for a dataset with multiple categorical and numerical features?

@davtg8172 Год назад

Thx Greg ! It's a very good tutorial from pyspark ! comprehensive with a lot of examples

@GregHogg Год назад

Glad to hear it :)

@maximinmaster7511 2 года назад

Thank you for this tutorial on PySpark !

@GregHogg 2 года назад

You're very welcome 🙂

@drjabirrahman 2 года назад

Good information Greg! Thanks for sharing.

@GregHogg 2 года назад

Glad to hear it! You're very welcome

@Value_Pilgrim 2 года назад

Thanks. That was pretty comprehensive.

@GregHogg 2 года назад

Glad to hear it!

@suman14san Год назад

Fantastic tutorial.

@user-kv2mn8bo4z 4 месяца назад

This is really helpful. Thank U

@GregHogg 4 месяца назад

Super glad to hear it, you're very welcome! Thanks so much for the support ❤️

@arsheyajain7055 2 года назад

Oh awesome thanks!

@GregHogg 2 года назад

No prob 😊

@GregThatcher 2 месяца назад

Thanks!

@GregHogg 2 месяца назад

Greg! You're too nice hahaha

@ammaralhawashem560 3 месяца назад

Thank you I just found one thing is confusing which is that you did the standard scaling AFTER the merging into one column shouldn't have you done it for each column before the merging?

@GregHogg 3 месяца назад

I don't remember sorry. But you're probably right

@ammaralhawashem560 3 месяца назад

@@GregHogg Thankyou for your reply I have done an experiment; in order to observe, I tried two features with large difference in values and use 5 million rows It seems even if we merge all the features before applying the scaling it will still calculate the parameters (mean & STD dev) for each feature In summary, you did NOT make any mistake