Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
Table of Contents (17 chapters)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Chapter 5: Building an AutoML Classification Solution

After building your AutoML regression solution with Python in Chapter 4, Building an AutoML Regression Solution, you should be feeling confident in your coding abilities. In this chapter, you will build a classification solution. Unlike regression, classification is used to predict the category of the object of interest. For example, if you're trying to predict who is likely to become a homeowner in the next five years, classification is the right machine learning approach.

Binary classification is when you are trying to predict two classes, such as homeowner or not, while multiclass classification involves trying to predict three or more classes, such as homeowner, renter, or lives with family. You can utilize both of these techniques with Azure AutoML, and this chapter will teach you how to train both kinds of models using different datasets.

In this chapter, you will begin by navigating directly to the Jupyter environment...