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.
In Part 1, Jennifer Marsman demonstrates 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. Then, join us for Part 2, in which we train a model with a machine learning algorithm, deploy our model, and call our published model to get results.
0:32: Using Kaggle to get a dataset
1:50: Intro to Azure Machine Learning Studio and how to upload a dataset
3:04: Create a new experiment in Azure Machine Learning Studio
4:21: Explore the data as a data scientist would, and think about how to clean it
18:22: Implement this data cleaning in Azure Machine Learning Studio
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