Machine Learning Pipeline Tutorial with Azure ML Studio

In this guided tutorial, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual's income. It predicts whether an individual's annual income is greater than or less than $50,000. The estimator used in this tutorial is a Two-Class Boosted Decision Tree classifier. Some of the features used to train the model are age, education, occupation, etc. Once you have scored and evaluated the model on the test data, you will deploy the trained model as an Azure Machine Learning web service. In just under an hour, you will be able to send new data to the web service API and receive the resulting predictions. This is the second course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This tutorial works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.