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Automated Machine Learning with Microsoft Azure

You're reading from  Automated Machine Learning with Microsoft Azure

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800565319
Pages 340 pages
Edition 1st Edition
Languages
Author (1):
Dennis Michael Sawyers Dennis Michael Sawyers
Profile icon Dennis Michael Sawyers

Table of Contents (17) Chapters

Preface 1. Section 1: AutoML Explained – Why, What, and How
2. Chapter 1: Introducing AutoML 3. Chapter 2: Getting Started with Azure Machine Learning Service 4. Chapter 3: Training Your First AutoML Model 5. Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
6. Chapter 4: Building an AutoML Regression Solution 7. Chapter 5: Building an AutoML Classification Solution 8. Chapter 6: Building an AutoML Forecasting Solution 9. Chapter 7: Using the Many Models Solution Accelerator 10. Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions
11. Chapter 8: Choosing Real-Time versus Batch Scoring 12. Chapter 9: Implementing a Batch Scoring Solution 13. Chapter 10: Creating End-to-End AutoML Solutions 14. Chapter 11: Implementing a Real-Time Scoring Solution 15. Chapter 12: Realizing Business Value with AutoML 16. Other Books You May Enjoy

Chapter 3: Training Your First AutoML Model

With an Azure account, an Azure Machine Learning workspace, a compute cluster, and a basic understanding of how AutoML works, you're now ready to train your first automated machine learning model. It will be easy and straightforward: AutoML is an equalizer that enables even novices to create advanced models in mere minutes, and you will appreciate its power by the end of this chapter regardless of your background. Practice makes perfect, and this chapter is your first step toward becoming an AutoML practitioner.

You will begin this chapter by loading data from your local machine to your Azure Machine Learning Studio (AML Studio), selecting only the columns you need for training. Then, you will proceed to train an AutoML model using the guided user interface. After training your model, you will learn how to interpret your results directly from the AMLS portal. This includes not only standard metrics such as accuracy, false positive...

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