Change Your World With Data!
In this AI workshop Direct Marketing, you are going to build a model to predict whether customers will visit a store based on an email campaign experiment. Some people received a campaign focused on women, some received a campaign focused on men, and some did not receive an email at all.
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Are you familiar with The Simpsons? Do you know the main characters? In case you don’t, this workshop solves your problem! In this AI workshop you learn how to build a model to recognize the main characters of The Simpsons in 5 steps with an image dataset and Microsoft’s Custom Vision service. You need an Azure subscription to do so. If you don’t have one, you can start a trial here.
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In this AI workshop, you are going to build a model to predict the bike demand for a specific hour of a day for the city of Washington. The data is available as sample data in the Azure ML Studio (classic) and is based on the data that has been collected in 2011 and 2012 in Washington.
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This workshop is about building a model to classify people using demographics to predict whether a person will have an annual income over 50K dollars or not.
The dataset used in this experiment is the US Adult Census Income Binary Classification dataset, which is a subset of the 1994 Census database, using working adults over the age of 16 with an adjusted income index of > 100.
This blog is inspired on the Sample 5: Binary Classification with Web Service: Adult Database from the Azure AI Gallery.
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Imagine you are an HR-Manager, and you would like to know which employees are likely to stay, and which might leave your company. Besides you would like to understand which factors contribute to leaving your company. You have gathered data in the past (well, in this case Kaggle simulated a dataset for you, but just imagine), and now you can start with this Predict Employee Leave Hands-On Lab to build your prediction model to see if that can help you.
In this lab, you will learn how to create a machine learning module with Azure Machine Learning Studio that predicts whether an employee will stay or leave your company. We are aware of the limitations of the dataset but the objective of this hands-on lab is to inspire you to explore the possibilities of using machine learning for your own research, and not to build the next HR-solution.
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