My goal for the week was to get familiar with creating and publishing machine learning experiments using Azure ML. I have been on the road this week and there was plenty of opportunity to do this while waiting for flights and trains and while on them. In a previous blog post, I listed out the resources that I used to get ramped up on Machine Learning itself. You can see the post at LINK. In addition, also have a look at the following blog posts:
- Big Data & Machine Learning Scenarios for Retail
- Some Azure Machine Learning Implementations in the Retail Industry
- Machine Learning API’s for Retail
- Microsoft Azure Machine Learning Service and what it means to the Retail Industry
And now, here are a list of resources that I used for getting up to speed on Azure ML:
|In this demo-rich course, led by entertaining experts Buck Woody, Seayoung Rhee, and Scott Klein, get a real-world look at the different ways you can efficiently embed predictive analytics in your big data solutions, and explore best practices for analyzing trends and patterns. Find out about extending Azure ML using the Azure ML API services, and look at scenarios and methods for monetizing your ML application with Azure Marketplace.
|Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes||The book provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read.|
|Data Science in the Cloud, with Azure Machine Learning and R||This report uses an in-depth data science example — predicting bicycle rental demand — to show you how to perform basic data science tasks, including data management, data transformation, machine learning, and model evaluation in the Microsoft Azure Machine Learning cloud environment. Using a free-tier Azure ML account, example R scripts, and the data provided, the report provides hands-on experience with this practical data science example.|
There are plenty of other resources available on Channel 9 and other sources but the ones above is what I used for my learning process while I was on the road this week. This blog post was composed at the Geneva station, while waiting for my train to Cologne. Feel free to send me a note if you find other resources that are an absolute must read. Thank you.