Build The Pipeline

Lesson 8 of 10

In this assignment you are going to build an Azure Machine Learning pipeline that loads the California Housing dataset and transforms any columns that require extra processing. Our objective is to get the data ready for machine learning training.

Your first task is to start the Azure Machine Learning designer and use it to create the pipeline.

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Please provide your access code to unlock and read the remainder of this lesson. Your code will be securely stored in your browser, so you only need to enter it once. If you do not have an access code for this lab, subscribe to all-access membership or buy the course to get your code.

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