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
Showcase this hands-on experience in an interview
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.
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
Introduction and Project Overview
Data Cleaning
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
您的工作空间就是浏览器中的云桌面,无需下载
在分屏视频中,您的授课教师会为您提供分步指导
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.
指导项目可在台式设备和移动设备上学习吗?
由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面,因此指导项目不在移动设备上提供。
指导项目的讲师是谁?
指导项目讲师是特定领域的专家,他们在项目的技能、工具或领域方面经验丰富,并且热衷于分享自己的知识以影响全球数百万的学生。
我能在完成指导项目后从中下载作品吗?
您可以从指导项目中下载并保留您创建的任何文件。为此,您可以在访问云桌面时使用‘文件浏览器’功能。
我需要具备多少经验才能做这个指导项目?
您可在页面顶部点按此指导项目的经验级别,查看任何知识先决条件。对于指导项目的每个级别,您的讲师会逐步为您提供指导。
我能直接通过 Web 浏览器来完成此指导项目,而不必安装特殊软件吗?
是,您可以在浏览器的云桌面中获得完成指导项目所需的一切。
指导项目的学习体验如何?
您可以直接在浏览器中于分屏环境下完成任务,以此从做中学。在屏幕的左侧,您将在工作空间中完成任务。在屏幕的右侧,您将看到有讲师逐步指导您完成项目。
还有其他问题吗?请访问 学生帮助中心。