Programming ML.NET

Programming ML.NET

by Dino Esposito, Francesco Esposito
Released March 2022
Publisher(s): Microsoft Press
ISBN: 9780137383511

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Book description

With .NET 5s ML.NET and Programming ML.NET, any Microsoft .NET developer can solve serious machine learning problems, increasing their value and competitiveness in some of todays fastest-growing areas of software development. World-renowned Microsoft development expert Dino Esposito covers everything you need to know about ML.NET, the machine learning pipeline, and real-world machine learning solutions development.

Modeled on his popular Programming ASP.NET books, this guide takes the same scenario-based approach Microsofts team used to build the ML.NET framework itself. Esposito presents and illuminates ML.NETs dedicated mini-frameworks (ML Tasks) for specific classes of problems, and draws on personal experience to help developers apply these in the real world, where a problems complexity can vary widely based on data availability or the specific results you need. In a full section on ML.NET neural networks, Esposito introduces key concepts and presents realistic examples you can reuse in your own applications. Along the way, Esposito also shows how to leverage powerful Python-based machine learning tools in the .NET environment.

Programming ML.NET will help you add machine learning and artificial intelligence to your tool belt, whether you have a background in these high-demand technologies or not.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents
  6. Contents at a Glance
  7. Acknowledgments
  8. Introduction
    1. Who Should Read This Book?
    2. Who Should Not Read This Book?
    3. Organization of This Book
    4. System Requirements
    5. Code Samples
    6. Errata, updates, & book support
    7. Stay in Touch
  9. Chapter 1. Artificially Intelligent Software
    1. How We Ended Up with Software
    2. The Role of Software Today
    3. AI Is Just Software
  10. Chapter 2. An Architectural Perspective of ML.NET
    1. Life Beyond Python
    2. Introducing ML.NET
    3. Consuming a Trained Model
    4. Summary
  11. Chapter 3. The Foundation of ML.NET
    1. On the Way to Data Engineering
    2. The Data to Start From
    3. The Training Step
    4. Consuming the Model from a Client Application
    5. Summary
  12. Chapter 4. Prediction Tasks
    1. The Pipeline and the Chain of Estimators
    2. The Regression ML Task
    3. Using the Regression Task
    4. The ML Devil’s Advocate
    5. Summary
  13. Chapter 5. Classification Tasks
    1. The Binary Classification ML Task
    2. Binary Classification for Sentiment Analysis
    3. The Multiclass Classification ML Task
    4. Using the Multiclass Classification Task
    5. The ML Devil’s Advocate
    6. Summary
  14. Chapter 6. Clustering Tasks
    1. The Clustering ML Task
    2. The ML Devil’s Advocate
    3. Summary
  15. Chapter 7. Anomaly Detection Tasks
    1. What Is an Anomaly?
    2. General Approaches to Detect Anomalies
    3. The Anomaly Detection ML Task
    4. The ML Devil’s Advocate
    5. Summary
  16. Chapter 8. Forecasting Tasks
    1. Predicting the Future
    2. The Forecast ML Task
    3. The ML Devil’s Advocate
    4. Summary
  17. Chapter 9. Recommendation Tasks
    1. Inside Information Retrieval Systems
    2. The ML Recommendation Task
    3. ML Devil’s Advocate
    4. Summary
  18. Chapter 10. Image Classification Tasks
    1. Transfer Learning
    2. Transfer Learning via Composition
    3. The ML Image Classification Task
    4. The ML Devil’s Advocate
    5. Summary
  19. Chapter 11. Overview of Neural Networks
    1. Feed-forward Neural Networks
    2. More Sophisticated Neural Networks
    3. Summary
  20. Chapter 12. A Neural Network to Recognize Passports
    1. Using Azure Cognitive Services
    2. Crafting Your Own Neural Network
    3. The ML Devil’s Advocate
    4. Summary
  21. Appendix A. Model Explainability
    1. Software Intelligence
    2. The Super Theory of Artificial Intelligence
    3. Machine Learning Black Boxes
    4. Interpretability and Explainability
    5. Explainability Techniques
    6. Conclusion
  22. Index
  23. Code Snippets

Product information

  • Title: Programming ML.NET
  • Author(s): Dino Esposito, Francesco Esposito
  • Release date: March 2022
  • Publisher(s): Microsoft Press
  • ISBN: 9780137383511