Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

Cover
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Table of Contents
Preface
Section 1: Fundamentals of Machine Learning and ML.NET
Chapter 1: Getting Started with Machine Learning and ML.NET
	The importance of learning about machine learning today
	The model building process
		Defining your problem statement
		Defining your features
		Obtaining a dataset
		Feature extraction and pipeline
		Model training
		Model evaluation
	Exploring types of learning
		Supervised learning
		Unsupervised learning
	Exploring various machine learning algorithms
		Binary classification
		Regression
		Anomaly detection
		Clustering
		Matrix factorization
	What is ML.NET?
		Technical details of ML.NET
		Components of ML.NET
		Extensibility of ML.NET
	Summary
Chapter 2: Setting Up the ML.NET Environment
	Setting up your development environment
		Installing Visual Studio
		Installing .NET Core 3
		Creating a process
	Creating your first ML.NET application
		Creating the project in Visual Studio
		Project architecture
		Running the code
			The RestaurantFeedback class
			The RestaurantPrediction class
			The Trainer class
			The Predictor class
			The BaseML class
			The Program class
		Running the example
	Evaluating the model
	Summary
Section 2: ML.NET Models
Chapter 3: Regression Model
	Breaking down regression models
		Choosing the type of regression model
		Choosing a linear regression trainer
		Choosing a logistic regression trainer
	Creating the linear regression application
		Diving into the trainer
		Exploring the project architecture
		Diving into the code
			The ExtensionMethods class
			The EmploymentHistory class
			The EmploymentHistoryPrediction class
			The Predictor class
			The Trainer class
			The Program class
		Running the application
	Creating the logistic regression application
		Exploring the project architecture
		Diving into the code
			The FeatureExtractor class
			The FileInput class
			The FilePrediction class
			The BaseML class
			The Predictor class
			The Trainer class
			The Program class
		Running the application
	Evaluating a regression model
		Loss function
		Mean squared error
		Mean absolute error
		R-squared
		Root mean squared error
	Summary
Chapter 4: Classification Model
	Breaking down classification models
		Choosing a classification trainer
	Creating a binary classification application
		Diving into the trainer
		Exploring the project architecture
		Diving into the code
		The CarInventory class
		The CarInventoryPrediction class
		The Predictor class
		The Trainer class
		The Program class
		Running the application
	Creating a multi-class classification application
		Diving into the trainer
		Exploring the project architecture
		Diving into the code
		The Email class
		The EmailPrediction class
		The Predictor class
		The Trainer class
		Running the application
	Evaluating a classification model
		Accuracy
		Area Under ROC Curve
		F1 Score
		Area Under Precision-Recall Curve
		Micro Accuracy
		Macro Accuracy
		Log Loss
		Log-Loss Reduction
	Summary
Chapter 5: Clustering Model
	Breaking down the k-means algorithm
		Use cases for clustering
		Diving into the k-means trainer
	Creating the clustering application
		Exploring the project architecture
		Diving into the code
		The Constants class
		The BaseML class
		The FileTypes enumeration
		The FileData class
		The FileTypePrediction class
		The FeatureExtractor class
		The Predictor class
		The Trainer class
		The Program class
		Running the application
	Evaluating a k-means model
		Average distance
		The Davies-Bouldin Index
		Normalized mutual information
	Summary
Chapter 6: Anomaly Detection Model
	Breaking down anomaly detection
		Use cases for anomaly detection
		Diving into the randomized PCA trainer
		Diving into time series transforms
	Creating a time series application
		Exploring the project architecture
		Diving into the code
		The NetworkTrafficHistory class
		The NetworkTrafficPrediction class
		The Predictor class
		The Trainer class
		The Program class
		Running the application
	Creating an anomaly detection application
		Exploring the project architecture
		Diving into the code
		The Constants class
		The LoginHistory class
		The LoginPrediction class
		The Predictor class
		The Trainer class
		Running the application
	Evaluating a randomized PCA model
		Area under the ROC curve
		Detection rate at false positive count
	Summary
Chapter 7: Matrix Factorization Model
	Breaking down matrix factorizations
		Use cases for matrix factorizations
		Diving into the matrix factorization trainer
	Creating a matrix factorization application
		Exploring the project architecture
		Diving into the code
			The MusicRating class
			The MusicPrediction class
			The Predictor class
			The Trainer class
			The Constants class
		Running the application
	Evaluating a matrix factorization model
		Loss function
		MSE
		MAE
		R-squared 
		RMSE
	Summary
Section 3: Real-World Integrations with ML.NET
Chapter 8: Using ML.NET with .NET Core and Forecasting
	Breaking down the .NET Core application architecture
		.NET Core architecture
		.NET Core targets
		.NET Core future
	Creating the stock price estimator application
		Exploring the project architecture
		Diving into the code
		The ProgramActions enumeration
		The CommandLineParser class
		The BaseML class
		The StockPrediction class
		The StockPrices class
		The Predictor class
		The Trainer class
		The ProgramArguments class
		The Program class
		Running the application
	Exploring additional production application enhancements
		Logging
		Utilizing Reflection further
		Utilizing a database
	Summary
Chapter 9: Using ML.NET with ASP.NET Core
	Breaking down ASP.NET Core
		Understanding the ASP.NET Core architecture
			Controllers
			Models
			Views
		Blazor
	Creating the file classification web application
		Exploring the project architecture
		Diving into the library
			The FileClassificationResponseItem class
			The FileData class
			The FileDataPrediction class
			The Converters class
			The ExtensionMethods class
			The HashingExtensions class
			The FileClassificationFeatureExtractor class
			The FileClassificationPredictor class
			The FileClassificationTrainer class
		Diving into the web application
			The UploadController class
			The Startup class
			The Index.razor file
		Diving into the trainer application
			The ProgramArguments class
			The ProgramActions enumeration
			The Program class
		Running the trainer application
		Running the web application
	Exploring additional ideas for improvements
		Logging
		Utilizing a caching layer
		Utilizing a database
	Summary
Chapter 10: Using ML.NET with UWP
	Breaking down the UWP architecture
		Views
		Models
		View Models
	Creating the web browser classification application
		Exploring the project architecture
		Diving into the library
			The Constants class
			The WebPageResponseItem class
			The Converters class
			The ExtensionMethods class
			The WebPageInputItem class
			The WebPagePredictionItem class
			The WebContentFeatureExtractor class
			The WebContentPredictor class
			The WebContentTrainer class
		Diving into the UWP browser application
			The MainPageViewModel class
			MainPage.xaml
			MainPage.xaml.cs
		Diving into the trainer application
			The ProgramArguments class
			The Program class
		Running the trainer application
		Running the browser application
	Additional ideas for improvements
		Single-download optimization
		Logging
		Utilizing a database
	Summary
Section 4: Extending ML.NET
Chapter 11: Training and Building Production Models
	Investigating feature engineering
		PNG image files with embedded executables
			Creating a PNG parser
	Obtaining training and testing datasets
	Creating your model-building pipeline
		Discussing attributes to consider in a pipeline platform
		Exploring machine learning platforms
			Azure Machine Learning
			Apache Airflow
			Apache Spark
	Summary
Chapter 12: Using TensorFlow with ML.NET
	Breaking down Google's Inception model
	Creating the WPF image classification application
		Exploring the project architecture
		Diving into the WPF image classification application
			The MainWindowViewModel class
			The MainWindow.xaml class
			The MainWindow.xaml.cs file
			The BaseML class
			The ImageDataInputItem class
			The ImageDataPredictionItem class
			The ImageClassificationPredictor class
		Running the image classification application
	Additional ideas for improvements
		Self-training based on the end user's input
		Logging
		Utilizing a database
	Summary
Chapter 13: Using ONNX with ML.NET
	Breaking down ONNX and YOLO
		Introducing ONNX
		The YOLO ONNX model
	Creating the ONNX object detection application
		Exploring the project architecture
		Diving into the code
			The DimensionsBase class
			The YoloBoundingBox class
			The MainWindow.xaml file
			The ImageClassificationPredictor class
			The MainWindowViewModel class
		Running the application
	Exploring additional production application enhancements
		Logging
		Image scaling
		Utilizing the full YOLO model
	Summary
Other Books You May Enjoy
Index