Ultimate Machine Learning with ML.NET

✍ Scribed by Kalicharan Mahasivabhattu; Deepti Bandi

• Publisher: Orange Education Pvt Ltd, AVA™
• Year: 2024
• Tongue: English
• Leaves: 224
• Category: Library

✦ Synopsis

“Empower Your .NET Journey with Machine Learning”Book DescriptionDive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET.The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of machine learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities.It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse.The final section of the book covers advanced techniques for optimizing model accuracy and refining performance. You will be able to deploy your ML.NET models using Azure Functions and Web API, empowering you to integrate machine learning solutions seamlessly into real-world applications.Table of Contents1. Introduction to ML.NET2. Installing and Configuring ML.NET3. ML.NET Model Builder and CLI4. Collecting and Preparing Data for ML.NET5. Machine Learning Tasks in ML.NET6. Choosing and Tuning Machine Learning Algorithms in ML.NET7. Inspecting and Interpreting ML.NET Models8. Saving and Loading Models in ML.Net9. Optimizing ML.NET Models for Accuracy10. Deploying ML.NET Models with Azure Functions and Web API Index

✦ Table of Contents

Cover Page
Title Page
Copyright Page
Dedication Page
About the Authors
About the Technical Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Introduction to ML.NET
Introduction
Structure
The Significance of Machine Learning
Introducing ML.NET
Overview of ML.NET
Genesis of ML.NET
History and Development of ML.NET
ML.NET’s Core Features
Supported Languages and Platforms
Licensing and Community Involvement
Machine Learning Concepts and Terminology
Understanding Machine Learning
Basic Machine Learning Terminology
Supervised Learning, Unsupervised Learning, and Other Paradigms
Common Tasks in Machine Learning
Use Cases for ML.NET
Healthcare
Finance
E-commerce
Manufacturing and Industry
Case Studies and Benefits
Impact on Decision-Making and Efficiency
Comparing ML.NET with Other Machine Learning Frameworks
Comparison with TensorFlow and PyTorch
Comparison with Scikit-learn
Strengths and Weaknesses of ML.NET
Consideration of Factors
Basic Workflow for Building a Machine Learning Model with ML.NET
Data Preparation
Model Training
Model Evaluation and Tuning
Model Deployment and Integration
Conclusion
2. Installing and Configuring ML.NET
Introduction
Structure
System Requirements for ML.NET
Minimum Hardware Specifications
Supported Operating Systems
Prerequisites for Windows, Linux, and macOS
Installing ML.NET on Different Operating Systems
Installing ML.NET on Windows
Installing ML.NET on macOS and Linux
Verifying the Installation
macOS-Specific Considerations
Configuring the ML.NET Environment
Setting Up Dependencies and Packages for ML.NET
Troubleshooting Common Installation Issues
Common Pitfalls and Their Solutions
Conclusion
3. ML.NET Model Builder and CLI
Introduction
Structure
Introducing ML.NET Model Builder and CLI
Benefits
Model Builder-Installation and Setup
Building a Machine Learning Model with Model Builder
Installing CLI
Using the CLI to Automate Machine Learning Tasks
Debugging and Troubleshooting using CLI
Common Errors and Solutions
Debugging Techniques
Logging and Output Inspection
Profiling and Performance Optimization
Best Practices and Tips
Workflow Efficiency
Handling Large Datasets
Collaborative Development
Version Control and Reproducibility
Case Studies and Examples
Model Builder and CLI in Action
Conclusion
Recap of Model Builder and Key Features of CLI
References and Further Reading
4. Collecting and Preparing Data for ML.NET
Introduction
Structure
Data Collection
ML.NET’s Data Loading APIs
TextLoader
DatabaseLoader
Preprocessing for Quality and Completeness of Data
Data Cleaning
Data Encoding
Code for Data Loading and Cleaning in ML.NET
Feature Engineering Using ML.NET’s APIs
ML.NET’s Feature Engineering APIs
APIs to Engineer New Features from the Patient Data
Hands-on Exercises
Conclusion
Multiple Choice Questions
Answers
5. Machine Learning Tasks in ML.NET
Introduction
Structure
Machine Learning Tasks in ML.NET
Machine Learning Tasks
Understanding Machine Learning Tasks
The Significance of Machine Learning Tasks
Overview of ML.NET’s Capabilities for Different Tasks
Binary Classification
MultiClass Classification
Regression
Clustering
Anomaly Detection
Ranking
Recommendation
Forecasting
Use Cases for Machine Learning Tasks in ML.NET
Conclusion
Hands on Exercises
Multiple Choice Questions
Answers
6. Choosing and Tuning Machine Learning Algorithms in ML.NET
Introduction
Structure
Overview of Machine Learning Algorithms in ML.NET
Techniques for Choosing the Best Algorithm for a Given Task
Linear Algorithms
Averaged Perceptron for Text Classification in ML.NET
Stochastic Dual Coordinated Ascent in ML.NET
Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) in ML.NET
Symbolic Stochastic Gradient Descent in ML.NET
Online Gradient Descent in ML.NET
Decision Tree Algorithms in ML.NET
Light Gradient Boosted Machine (LightGBM) in ML.NET
FastTree in ML.NET
FastForest in ML.NET
Generalized Additive Model (GAM) in ML.NET
Matrix Factorization in ML.NET
Field-Aware Factorization Machine in ML.NET
Meta Algorithms in ML.NET
One-Versus-All (OvA) Classifier in ML.NET
Ordinary Least Squares (OLS) Regression in ML.NET
Pairwise Coupling Classifier in ML.NET
K-Means Clustering in ML.NET
Principal Component Analysis (PCA) in ML.NET
Naive Bayes for Multiclass Classification in ML.NET
Prior Trainer in ML.NET
Support Vector Machines (SVMs) in Machine Learning
Linear Support Vector Machine (SVM) in ML.NET
Local Deep SVM (LdSVM) in ML.NET
Additional Consideration
Iris Dataset
MNIST Dataset
Wine Quality Dataset
Aiding Algorithm Selection Using ML.net
Hyperparameters
Tuning Hyperparameters for Optimal Performance
Grid Search
Purpose of GridSearch
Random Search
Bayesian Optimization
Cross-Validation and Model Selection in ML.NET
K-Fold Cross-Validation
Leave-One-Out Cross-Validation
Stratified K-Fold Cross-Validation
Best Practices
Evaluating and Comparing Machine Learning Algorithms in ML.NET
Conclusion
7. Inspecting and Interpreting ML.NET Models
Introduction
Structure
Model Inspection and Interpretation in ML.NET
Need for Model Inspection and Interpretation
Model Inspection and Interpretation Metrics
Conclusion
Multiple Choice Questions
Answers
8. Saving and Loading Models in ML.Net
Introduction
Structure
Overview of Saving and Loading Models in ML.NET
Value of Saving and Loading Models
Reusing Pre-Trained Models
Serialization and Deserialization
Streamlining Development with Saving and Loading
File Formats for Saving ML.NET Models
Native ML.NET Binary Format
ONNX (Open Neural Network Exchange) Format
Choosing the Right Format
Code Example
Best Practices for Saving and Loading Models in ML.NET
Save Model Weights and Architecture Separately
Versioning Considerations
Naming Conventions and Folder Structures
Handling Common Issues During Loading
Troubleshooting Common Issues with Saving and Loading ML.NET Models
Version Mismatch
Architecture Changes
Data Processing Pipeline Discrepancies
File Corruption or Tampering
Missing or Incomplete Model Files
Conclusion
Multiple Choice Questions
Answers
9. Optimizing ML.NET Models for Accuracy
Introduction
Structure
Augment Data Samples
Add Context to the Data
Feature Selection
Cross-Validation
Limitations and Considerations
Hyperparameter Tuning
Choose a Different Algorithm
Challenges in Optimizing ML.NET Models
Overfitting
Class Imbalance and Data Leakage During Optimization
Debugging Optimization Issues
Importance of Domain Knowledge
Hands on Exercise
Conclusion
Multiple Choice Questions
Answers
10. Deploying ML.NET Models with Azure Functions and Web API
Introduction
Structure
Overview of Deploying ML.NET Models to the Cloud
Advantages of Deploying ML.NET Models to the Cloud
ML.NET and Cloud Deployment
Supported Cloud Platforms for ML.NET Deployment
Cloud Agnostic Approach
Using Azure Functions and Web API for Model Deployment
Introduction to Azure Functions
Advantages of Azure Functions for Model Deployment
Leveraging Web API for Model Access
Advantages of Web API for Model Deployment
Setting Up Azure Functions and Web API
Steps for Setting Up Azure Functions and Web API
Packaging and Deploying ML.NET Models as RESTful Services
Preparing the ML.NET Model for Deployment
Saving the ML.NET Model as ONNX
Deploying the ML.NET Model to Azure Functions
Steps to Deploy the ML.NET Model to Azure Functions
Implementing RESTful Endpoints for Model Consumption
Designing RESTful API Endpoints
Best Practices for Deploying ML.NET Models to Production Environments
Model Performance and Optimization
Scaling ML.NET Models on the Cloud
Deployment Security and Compliance
Monitoring and Logging
Versioning and Rollbacks
Monitoring and Troubleshooting Deployed ML.NET Models
Monitoring Model Performance and Usage
Troubleshooting Common Deployment Issues
Health Checks and Failover Mechanisms
Conclusion
Index