Practical automated machine learning on Azure : using Azure machine learning to quickly build AI solutions / Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok.

Format:

Book

Author/Creator:

Mukunthu, Deepak, author.

Shah, Parashar, author.

Tok, Wee-Hyong, author.

Language:

English

Subjects (All):

Machine learning.

Windows Azure.

Physical Description:

1 online resource (199 pages)

Edition:

First edition.

Place of Publication:

Beijing : O'Reilly, [2019]

System Details:

text file

Summary:

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine learning models is an iterative and time-consuming process. Even those who know how to create these models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply Automated Machine Learning to your data right away. Learn how companies in different industries are benefiting from Automated Machine Learning Get started with Automated Machine Learning using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professionals, and developers can use Automated Machine Learning in their familiar tools and experiences Learn how to get started using Automated Machine Learning for use cases including classification and regression.

Contents:

Part 1. Automated machine learning. Machine learning : overview and best practices

How automated machine learning works

Part 2. Automated ML on Azure. Getting started with Microsoft Azure machine learning and automated ML

Feature engineering and automated machine learning

Deploying automated machine learning models

Classification and regression

Part 3. How enterprises are using automated machine learning. Model interpretability and transparency with automated ML

Automated ML for developers

Automated ML for everyone.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781492055549

1492055549

9781492055587

1492055581

9781492055563

1492055565

OCLC:

1122564740