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.
A basic understanding of machine learning workflows.
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
It was an excellent learning from a novice like me in the last part of the project I got lagged but the rest I learned thank you i hope i can attend more projects like this to gain more experience
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.
Amazing introduction to what can be done with Machine Learning for people who have no background in Machine Learning — and for those who have it too.
overall better time with the instructor through out the project timeline and the things given were too much overwhelming for us begineers!
指導 項目 可在台式設備和移動設備上學習嗎？
指導 項目 的講師是誰？
指導 項目 講師是特定領域的專家，他們在項目的技能、工具或領域方面經驗豐富，並且熱衷於分享自己的知識以影響全球數百萬的學生。
我能在完成指導 項目 後從中下載作品嗎？
您可以從指導 項目 中下載並保留您創建的任何文件。為此，您可以在訪問云桌面時使用'文件瀏覽器'功能。
您可在頁面頂部點按此指導 項目 的經驗級別，查看任何知識先決條件。對於指導 項目 的每個級別，您的講師會逐步為您提供指導。
我能直接通過 Web 瀏覽器來完成此指導 項目，而不必安裝特殊軟件嗎？
是，您可以在瀏覽器的雲桌面中獲得完成指導 項目 所需的一切。
指導 項目 的學習體驗如何？