Programming ML.NET

About

Features

• Clearly explains the ML.NET model, pipeline, and capabilities
• Provides end-to-end tutorials on common ML tasks, and realistic examples throughout
• Expert coverage of neural networks includes an expert executive summary and reusable samples
• By Dino Esposito, one of the world's most respected authors, trainers, and consultants on Microsoft development technologies

Description

The expert guide to creating production machine learning solutions with ML.NET!

ML.NET brings the power of machine learning to all .NET developers and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito's best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft's team used to build ML.NET itself. After a foundational overview of ML.NET's libraries, the authors illuminate mini-frameworks (ML Tasks) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network showing how to leverage popular Python tools within .NET.

14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to:

• Build smarter machine learning solutions that are closer to your user's needs
• See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction
• Implement data processing and training, and productionize machine learningbased software solutions
• Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification
• Perform both binary and multiclass classification
• Use clustering and unsupervised learning to organize data into homogeneous groups
• Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues
• Make the most of ML.NET's powerful, flexible forecasting capabilities
• Implement the related functions of ranking, recommendation, and collaborative filtering
• Quickly build image classification solutions with ML.NET transfer learning
• Move to deep learning when standard algorithms and shallow learning aren't enough
• Buy neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow

Downloads

Downloads

Follow the instructions to download this book's lesson files.

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Sample Content

Sample Pages

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Table of Contents

CHAPTER 1 Artificially Intelligent Software 

CHAPTER 2 An Architectural Perspective of ML.NET

CHAPTER 3 The Foundation of ML.NET

CHAPTER 4 Prediction Tasks

CHAPTER 5 Classification Tasks

CHAPTER 6 Clustering Tasks

CHAPTER 7 Anomaly Detection Tasks

CHAPTER 8 Forecasting Tasks

CHAPTER 9 Recommendation Tasks

CHAPTER 10 Image Classification Tasks

CHAPTER 11 Overview of Neural Networks

CHAPTER 12 A Neural Network to Recognize Passports

APPENDIX A Model Explainability

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