Hands-on Labs of Azure Machine Learning

Deploying a Model with Azure Machine Learning

This lab explores unsupervised learning in Azure Machine Learning and how to deploy a predictive model as a web service. The lab will walk through copying an experiment from the Azure Machine Learning Gallery into the ML Studio, creating a scoring experiment, deploying a model as a web service, and interacting with the API using the included web interface.

“Where should I open my next restaurant location?” This question is often very difficult to answer. The right choice could lead to increased revenue and profit, but the wrong choice could lead to losing a major investment. Trying to make this decision by manually sifting through hundreds or even thousands of possible cities or neighborhoods can be almost impossible. Machine learning can help with this task by analyzing large volumes of data about different locations, finding common characteristics among locations, and grouping those like-attributed locations together. These groups can then be compared to previously successful restaurant locations to help narrow the choices for where to open next. In this lab, you will work with a dataset that includes geographic, economic, and demographic data about different US cities. The model you will explore uses a K-Means algorithm to cluster cities into distinctive buckets.

 

Text Mining with R and Azure Machine Learning

This lab explores text analytics and R integration with Azure Machine Learning. It will walk through loading data from an external source, using R scripts in ML Studio, and common text analytics tasks and visualizations.

Social media has become a very influential platform for companies, consumers, and professionals to express ideas and opinions, market new products and advertise sales, or share any other important news and information.  Most social media sites include keywords or hashtags users can post related content to.  If companies can access and perform advanced analytics on the keyword posts that are relevant to them, they can learn things such as customer sentiment, related products and companies, and who is buying products and where from. For this lab, you will be working with real Twitter data pulled from a Twitter API.  The data includes real Tweets that used the hashtag, Azure. The R language has an expansive collection of packages and functions for advanced text mining and analytics.  The lab will use R scripts that will be executed in ML Studio.  These scripts will perform data preparation, exploration, and visualization tasks common to text mining. The end result will be a visualization that provides context to frequently used terms in the analyzed Tweets.