Change Your World With Data!
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.
Continue reading “AI Workshop: Recognize The Simpsons!”
In this AI workshop, you are going to build a model to predict student performance. The data has been collected during the 2005-2006 school year from two public schools from the Alentejo region of Portugal. We will look at their math performance.
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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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This blog is based on the Tech Tomorrow video hosted by Microsoft’s Stephanie Visser en Stijn Buiter. They explain how to build a House Sale Price prediction model with Azure Machine Learning. This model predicts the possible sale price of a house in Ames, Iowa. The corresponding dataset is available on Kaggle, as part of the House Prices: Advanced Regression Techniques competition and the data has been elaborated by Dean de Cock, who wrote also a very inspiring on how the handle the Ames Housing data. Continue reading “Tech Tomorrow – Build your own House Sale Price prediction model”