A large amount of time for machine learning tasks is spent understanding the data and getting it into the proper configuration to train the model. This is the Data Wrangling, Exploration, and Cleaning phase of the machine learning lifecycle. In Azure Machine Learning designer, many common data changing operations are provided as transform modules. In this lab, you will explore the Join Data module to gain a deeper understanding of the tools at your disposal.
Learning Objectives
Successfully complete this lab by achieving the following learning objectives:
- Set Up the Workspace
Log in and go to the Azure Machine Learning Studio workspace provided in the lab.
Create a Training Cluster of
D2instances.Create a new blank Pipeline in the Azure Machine Learning Studio Designer.
- Explore Join Data
Add IMDB Movie Titles and Movie Ratings dataset nodes to the canvas. Visualize these datasets to see if they have any common data. Note, these columns might not be named exactly the same in both datasets.
Using a Join Data transform node, combine the datasets on their shared data. This will be an Inner Join operation. Remove one of the columns containing the shared data.
Submit the Pipeline to perform the transformation.
- Visualize the Transformed Data
When the pipeline finishes, inspect the output of the Join Data node. Can you tell what movie is being reviewed now? Was the duplicate ID column removed successfully?
