Pre-process data using appropriate modules
Train and evaluate a boosted decision tree model on Azure ML Studio
Create scoring and predictive experiments
Deploy the trained model as an Azure web service
In this project-based course, 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 project 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 course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Project Overview
Accounting for Class Imbalance
Training a Two-Class Boosted Decision Tree Model and Hyperparameter Tuning
Scoring and Evaluating the Models
Publishing the Trained Model as a Web Service for Inference
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
I have learn most quality things and practical knowledge with machine learning pipelines with Azure ML studio which is very useful for our future & It can help me in my life.
Very interesting, the only issue was the visualization on my laptop screen (less than a half)
I m learn many things in the coursera. This is one of the best app provide for everyone.
Its great for my learning session Machine Learning Pipelines ! Thank for this course.
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