Programming Ml Net

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.

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 learning–based 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

Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online Who This Book Is For Developers who want to learn how to use and apply machine learning to enrich their applications

Machine Learning has become a fundamental and integral part of many novel business solutions. Until now, those with C#.NET Programming experience had to learn either R or Python to delve into the Machine Learning world. Fortunately, Microsoft has recently released ML.NET (version 1.2) Machine Learning package. C# .NET Programmers worldwide can now leverage their C#.NET experience to train, evaluate and build Machine Learning Models and solutions using Microsoft ML.NET package. Microsoft ML.NET package, available for download from https: //www.nuget.org, is an excellent collection of Machine Learning Algorithms covering a wide range of Machine Learning Tasks including Text Classification, Binary and Multi-class Classification, Regression, Cluster Analysis, Recommender, among others, And all of these algorithms can now be used for training, evaluating and using Machine Learning Models in C#.NET. Now, C#.NET Programmers can develop novel and intelligent Apps for Windows Desktop using their extensive C#.NET experience. Those who prefer to use Xamarin to develop cross-platform Apps for Android or IOS or MacOS using C#.Net can now incorporate Machine Learning Models directly in their Apps leveraging their C#.NET experience. Those who develop, using Unity 3D, games or Data Visualization applications can now incorporate Machine Learning Models in their games or applications using C#.NET. The possibilities are limited only by your imagination. In the 'Microsoft ML.NET Machine Learning for .NET Developers using C#.NET' book (Volume I), you will find C#.NET Programs that take you step-by-step in completing Machine Learning Model training, evaluation and use for specific tasks and algorithms. Along with step-by-step discussion of the C# Program for each Algorithm covered in the book, you will also find Demonstration Videos for each Chapter covering each Algorithm and showing what to do at each step. The book also provides full code-listing with comments for each Chapter. Additionally, you will be able to download the Chapter example and sample C#.NET programs from the Github repository for this book. This book assumes that you are familiar with Visual Studio 2019 and that you are somewhat comfortable with C#.NET Programming language at a fundamental level.With this book, you will learn: *To download and import Microsoft ML.NET package directly into your Visual Studio 2019 Solution*To add Training and Testing Data Sets to your Visual Studio 2019 Solution*To add and create C# classes that serve as Input and Output Data Model classes for your Machine Learning Model*To work with specific Algorithms for Binary Classification and Multi-class Classification*To perform Sentiment Analysis and Iris Flower Classification*To use and apply MLContext and IDataView objects in developing Machine Learning Models*To Evaluate Machine Learning Models using various Performance Metrics*To use and apply Trained Machine Learning Models for Prediction or Classification Tasks*To save Trained Machine Learning Models for application development at a later date*To create a Sentiment Analysis Windows .NET App that uses already trained Machine Learning Model

Your hands-on guide to Entity Framework fundamentals Expand your expertise—and teach yourself the fundamentals of the Microsoft ADO.NET Entity Framework 5. If you have previous programming experience but are new to the Entity Framework, this tutorial delivers the step-by-step guidance and coding exercises you need to master core topics and techniques. Discover how to: Access data in a managed way—using minimal code Apply three workflows supported by the Entity Framework Perform essential tasks with full automation in place Manipulate data with both LINQ and Entity SQL Create examples that rely on Table-Valued Functions Determine the remedies for Entity-specific exceptions Explore the use of optimistic and pessimistic concurrency Define mappings between your applications and data sources

Publisher's Note: Microsoft stops supporting .NET Core 3.1 in December 2022. The newer 7th edition of this book is available that covers .NET 7 (end-of-life May 2024) or .NET 6 (end-of-life November 2024), with C# 11 and EF Core 7. Key FeaturesBuild modern, cross-platform applications with .NET Core 3.0Get up to speed with C#, and up to date with all the latest features of C# 8.0Start creating professional web applications with ASP.NET Core 3.0Book Description In C# 8.0 and .NET Core 3.0 – Modern Cross-Platform Development, Fourth Edition, expert teacher Mark J. Price gives you everything you need to start programming C# applications. This latest edition uses the popular Visual Studio Code editor to work across all major operating systems. It is fully updated and expanded with new chapters on Content Management Systems (CMS) and machine learning with ML.NET. The book covers all the topics you need. Part 1 teaches the fundamentals of C#, including object-oriented programming, and new C# 8.0 features such as nullable reference types, simplified switch pattern matching, and default interface methods. Part 2 covers the .NET Standard APIs, such as managing and querying data, monitoring and improving performance, working with the filesystem, async streams, serialization, and encryption. Part 3 provides examples of cross-platform applications you can build and deploy, such as web apps using ASP.NET Core or mobile apps using Xamarin.Forms. The book introduces three technologies for building Windows desktop applications including Windows Forms, Windows Presentation Foundation (WPF), and Universal Windows Platform (UWP) apps, as well as web applications, web services, and mobile apps. What you will learnBuild cross-platform applications for Windows, macOS, Linux, iOS, and AndroidExplore application development with C# 8.0 and .NET Core 3.0Explore ASP.NET Core 3.0 and create professional web applicationsLearn object-oriented programming and C# multitaskingQuery and manipulate data using LINQUse Entity Framework Core and work with relational databasesDiscover Windows app development using the Universal Windows Platform and XAMLBuild mobile applications for iOS and Android using Xamarin.FormsWho this book is for Readers with some prior programming experience or with a science, technology, engineering, or mathematics (STEM) background, who want to gain a solid foundation with C# 8.0 and .NET Core 3.0.

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary "plumbing" that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online This book is for developers who want to learn how to use and apply machine learning to enrich their applications. Sudipta Mukherjee is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.