Machine Learning Pipelines with Azure ML Studio

What you’ll learn

  • 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

Skills you’ll practice

  • Category: Data ScienceData Science
  • Category: Machine LearningMachine Learning
  • Category: Data AnalysisData Analysis
  • Category: Binary ClassificationBinary Classification
  • Category: Azure Machine LearningAzure Machine Learning

Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies

About this Guided Project

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.Read more

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. •Introduction and Project Overview
  2. •Data Cleaning
  3. •Accounting for Class Imbalance
  4. •Training a Two-Class Boosted Decision Tree Model and Hyperparameter Tuning
  5. •Scoring and Evaluating the Models
  6. •Publishing the Trained Model as a Web Service for Inference

Recommended experience

A basic understanding of machine learning workflows.

8 project images

How you’ll learn

  • Skill-based, hands-on learningPractice new skills by completing job-related tasks.
  • Expert guidanceFollow along with pre-recorded videos from experts using a unique side-by-side interface.
  • No downloads or installation requiredAccess the tools and resources you need in a pre-configured cloud workspace.
  • Available only on desktopThis Guided Project is designed for laptops or desktop computers with a relia