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#Machine_Learning #Data_Science #Python #Deep_Learning #ML #DL #Py #Jupyter #Colab #Tutorial #Step_by_Step #TensorFlow #Spark #PySpark #Big_Data #Data #Neural_Networks #Data_Scientist #Sklearn #Scikit-Learn #Keras #NumPy #PandasКомментарии:
Fantastic tutorial.
ОтветитьThx Greg ! It's a very good tutorial from pyspark ! comprehensive with a lot of examples
ОтветитьI wish I had seen this when I took Econ 424(ml) at uw😂
ОтветитьGreat 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?
ОтветитьThank you for this tutorial on PySpark !
ОтветитьOh awesome thanks!
ОтветитьThanks. That was pretty comprehensive.
ОтветитьGood information Greg! Thanks for sharing.
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