Data Science in the Cloud with Microsoft Azure Machine Learning and Python

Take time to explore Microsoft’s Azure machine learning platform, Azure ML—a production environment that simplifies the development and deployment of machine learning models. In this O’Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML. The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You’ll also learn how to extend Azure ML with Python. Elston uses downloadable Python code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, you’ll learn how to publish your trained models as web services in the Azure cloud. - With this report, you’ll learn how to: - Navigate Azure ML Studio - Use the Python Script module - Load Python modules from a zip file - Use the Sweep Parameters module - Apply a SQL transformation - Use the Cross Validate Model module - Publish a scoring model as a web service to Excel - Use Jupyter Notebooks with Azure ML Copyright Table of Contents Chapter 1. Data Science in the Cloud with Microsoft Azure Machine Learning and Python Introduction Downloads Working Between Azure ML and Spyder Overview of Azure ML Azure ML Studio Getting Data In and Out of Azure ML Modules and Datasets Azure ML Workflows A Regression Example Problem and Data Overview A First Set of Transformations Exploring a Potential Interaction Investigating a New Feature A First Model Improving the Model and Transformations Filtering with SQLite Another Data Transformation Evaluating the Improved Model Improving Model Parameter Selection in Azure ML Cross Validation Some Possible Next Steps Publishing a Model as a Web Service Using Jupyter Notebooks with Azure ML Summary