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Soal Olimpiade Bahasa Indonesia Sd Puedo Simulator Sele davoyon



 


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Using data from Github Using data from Many data sources (one, two,...) **Using data from Github** This is the most easy way to quickly experiment with Machine Learning on a whole variety of data. * You can use a full feature Matrix with your data. + Exports your dataset from Github. + Imports your data to your Data Observatory. + Runs the notebook. .. image:: /_static/img/vm1.png * You can also create a custom dataset from a Github repository or a Google Sheet using the PySpark Notebooks. * You can also use a file with the existing dataset from your Github repository or your Google Sheet. * You can import the dataset as a DataFrame using a.csv file * Use functions to quickly convert text to numpy arrays (text, numbers, email, etc.). * Try to use the built in Dataset API functions. + Lets you get the label, features and the structure of a dataset. + Allows you to build a UDF. .. image:: /_static/img/vm2.png * You can also access a few specific dataset properties with a UDF. + Storing the parameters as attributes. + Label and structure (including NumberOfRows, NumberOfColumns, etc.) .. image:: /_static/img/vm3.png + You can also get the NumberOfRows and NumberOfColumns of the dataset using the dataset UDF. + The parameters are not accessible as attributes in the dataset. + They are accessible through a pyspark.sql.types.StructField function. * You can also use UDF to process your data. * You can access almost any attribute of a dataset. * You can use the number of rows as an attribute. * You can get the attribute names as well as the dataset. * You can access the fields as well as the structure. * You can use the function count (numberOfRecords), nunique (numberOfUniqueRecords) and mean (

 

 

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Soal Olimpiade Bahasa Indonesia Sd Puedo Simulator Sele davoyon
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