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In this session, you will learn to use Azure Machine Learning to make predictions. The example used is predicting whether a passenger on the Titanic will survive, given information like their age, gender, class of ticket, ticket fare, etc. But these same principles can be used to predict if someone will make a purchase online or whether a patient will be readmitted to a hospital in the next 30 days.
Previously in Part 1, Jennifer Marsman has already demonstrated how to upload a dataset into Azure Machine Learning Studio, explore the data and decide how to modify it, and use data cleaning modules to implement these changes. In this Part 2, we train a model with a machine learning algorithm, deploy our model, and call our published model to get results.
1:14: Split the data
4:00: Choose a machine learning algorithm
9:02: Implement in Azure Machine Learning Studio
12:50: Evaluate the models
16:14: Deploy a model
18:31: Call a webservice to access our published model
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