Programming ML.NET (Developer Reference)

562 Pages·2022·13.085 MB·English

by  Dino Esposito, Francesco Esposito

Programming ML.Net
Dino Esposito
Francesco Esposito
Programming ML.Net
Published with the authorization of Microsoft Corporation by:
Pearson Education, Inc.
Copyright © 2022 by Pearson Education, Inc.
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ISBN-13: 978-0-13-738365-8
ISBN-10: 0-13-738365-7
Library of Congress Control Number: 2021952995
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Editor-in-Chief
Brett Bartow
Executive Editor
Loretta Yates
Sponsoring Editor
Charvi Arora
Development Editor
Rick Kughen
Managing Editor
Sandra Schroeder
Senior Project Editor
Tracey Croom
Copy Editor
Rick Kughen
Indexer
Timothy Wright
Proofreader
Abigail Manheim
Technical Editor
Bri Achtman
Cover Designer
Twist Creative, Seattle
Compositor
codeMantra
To Silvia, Michela and new dreams
— Dino Esposito
To my loved ones, to whom I couldn’t help but dedicate a book
— Francesco Esposito
Contents at a Glance
Acknowledgments
Introduction
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
Index
Contents
Acknowledgments
Introduction
Chapter 1 Artificially Intelligent Software
How We Ended Up with Software
The Formalization of Computing Machines
The Engineering of Computing Machines
The Birth of Artificial Intelligence
Software as a Side Effect
The Role of Software Today
Automating Tasks
Mirroring the Real World
Empowering People
AI Is Just Software
Chapter 2 An Architectural Perspective of ML.NET
Life Beyond Python
Why Is Python So Popular in Machine Learning?
Taxonomy of Python Machine Learning Libraries
End-to-End Solutions on Top of Python Models
Introducing ML.NET
The Learning Pipeline in ML.NET
Model Training Executive Summary
Consuming a Trained Model
Making the Model Callable from the Outside
Other Deployment Scenarios
From Data Science to Programming
Summary
Chapter 3 The Foundation of ML.NET
On the Way to Data Engineering
The Role of a Data Scientist
The Role of a Data Engineer
The Role of an ML Engineer
The Data to Start From
Making Sense of the Available Data
Building a Data Processing Pipeline
The Training Step
Picking an Algorithm
Measuring the Actual Value of an Algorithm
Planning the Testing Phase
A Look at the Metrics
Consuming the Model from a Client Application
Getting the Model File
The Overall Project
Making a Taxi Fare Prediction
Scalability Concerns
Devising an Adequate User Interface
Summary
Chapter 4 Prediction Tasks
The Pipeline and the Chain of Estimators
Data Views
Transformers
Estimators
Pipelines
The Regression ML Task
General Aspects of ML Tasks
Supported Regression Algorithms
Supported Validation Techniques
Using the Regression Task
A Look at the Available Training Data
Feature Engineering
Accessing the Content of Datasets
Composing the Training Pipeline
The ML Devil’s Advocate
Simple and Linear Regression
Nonlinear Regression
Summary
Chapter 5 Classification Tasks
The Binary Classification ML Task
Supported Algorithms
Supported Validation Techniques
Binary Classification for Sentiment Analysis
A Look at the Available Training Data
Feature Engineering
Composing the Training Pipeline
The Multiclass Classification ML Task
Supported Algorithms
Using the Multiclass Classification Task