Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions

Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions
PDF, ePUB
  • eBook:
    Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions
  • Author:
    Deepak Mukunthu, Parashar Shah, Wee Hyong Tok
  • Edition:
    1 edition
  • Categories:
  • Data:
    2019-10-08
  • ISBN:
    149205559X
  • ISBN-13:
    9781492055594
  • Language:
    English
  • Pages:
    198
  • Format:
    PDF, ePUB

Book Description
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 (AutoML), 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 ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away.
  • Learn how companies in different industries are benefiting from AutoML
  • Get started with AutoML using Azure
  • Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning
  • Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences
  • Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

Content

Part I. Automated Machine Learning
Chapter 1. Machine Learning: Overview and Best Practices
Chapter 2. How Automated Machine Learning Works

Part II. Automated ML on Azure
Chapter 3. Getting Started with Microsoft Azure Machine Learning and Automated ML
Chapter 4. Feature Engineering and Automated Machine Learning
Chapter 5. Deploying Automated Machine Learning Models
Chapter 6. Classification and Regression

Part III. How Enterprises Are Using Automated Machine Learning
Chapter 7. Model Interpretability and Transparency with Automated ML
Chapter 8. Automated ML for Developers
Chapter 9. Automated ML for Everyone

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