Overview

Machine learning, which facilitates predictive analytics from large volumes of data by employing algorithms that iteratively learn from that data, is one of the fastest growing areas of computer science. Its uses range from credit-card fraud detection and self-driving cars to optical character recognition (OCR) and online shopping recommendations. It makes us smarter by making computers smarter. And its usefulness will only increase as more and more data becomes available and our desire to perform predictive analytics from that data grows, too.

Azure Machine Learning is a cloud-based predictive-analytics service that offers a streamlined experience for data scientists of all skill levels. It's accompanied by the Azure Machine Learning Studio (ML Studio), which is a browser-based tool that provides an easy to use, drag-and-drop interface for building machine-learning models. It comes with a library of time-saving experiments and features best-in-class algorithms developed and tested in the real world by Microsoft businesses such as Bing. And its built-in support for R and Python means you can include scripts of your own to customize your model. Once you've built and trained your model in the ML Studio, you can easily expose it as a Web service that is consumable using a variety of programming languages, or share it with the community by placing it in the Cortana Intelligence Gallery.

In this lab, you will use Azure Machine Learning to model automobile features and prices and generate price predictions from feature inputs. Then you will deploy the model as a Web service and test it by placing calls to it.

Objectives

In this hands-on lab, you will learn how to:

  • Work with Azure Machine Learning Studio
  • Prepare input data and use it to train a model
  • Apply and test learning algorithms
  • Score a model and evaluate its accuracy
  • Deploy a model as a Web service

Prerequisites

The following is required to complete this hands-on lab:


Exercises