Note: We'd love to hear your thoughts about MLOps. Let us know in this survey.
ML.NET Samples
ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers.
In this GitHub repo, we provide samples which will help you get started with ML.NET and how to infuse ML into existing and new .NET apps.
Note: Please open issues related to ML.NET framework in the Machine Learning repository. Please create the issue in this repo only if you face issues with the samples in this repository.
There are two types of samples/apps in the repo:
Getting Started : ML.NET code focused samples for each ML task or area, usually implemented as simple console apps.
End-End apps : End-user sample web and desktop apps infused with Machine Learning models based on ML.NET.
The official ML.NET samples are divided in multiple categories depending on the scenario and machine learning problem/task, accessible through the following tables:
| Binary classification | ||
Sentiment Analysis C# F# |
Spam Detection C# F# |
Credit Card Fraud Detection (Binary Classification) C# F# |
Heart Disease Prediction C# |
||
| Multi-class classification | ||
Issues Classification C# F# |
Iris Flowers Classification C# F# |
MNIST C# |
| Recommendation | ||
Product Recommendation C# |
Movie Recommender (Matrix Factorization) C# |
Movie Recommender (Field Aware Factorization Machines) C# |
| Regression | ||
Price Prediction C# F# |
Sales Forecasting (Regression) C# |
Demand Prediction C# F# |
| Time Series Forecasting | ||
Sales Forecasting (Time Series) C# |
||
| Anomaly Detection | ||
Sales Spike Detection C# C# |
Power Anomaly Detection C# |
Credit Card Fraud Detection (Anomaly Detection) C# |
| Clustering | ||
Customer Segmentation C# F# |
IRIS Flowers Clustering C# F# |
|
| Ranking | ||
Rank Search Engine Results C# |
||
| Computer Vision | ||
Image Classification Training (High-Level API) C# F# |
Image Classification Predictions (Pretrained TensorFlow model scoring) C# F# C# |
Image Classification Training (TensorFlow Featurizer Estimator) C# F# |
Object Detection (ONNX model scoring) C# C# |
||
| Cross Cutting Scenarios | ||
Scalable Model on WebAPI C# |
Scalable Model on Razor web app C# |
Scalable Model on Azure Functions C# |
Scalable Model on Blazor web app C# |
Large Datasets C# |
Loading data with DatabaseLoader C# |
Loading data with LoadFromEnumerable C# |
Model Explainability C# |
Export to ONNX C# |
Automate ML.NET models generation (Preview state)
The previous samples show you how to use the ML.NET API 1.0 (GA since May 2019).
However, we're also working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best" model and the code for running it will be generated for you.
These additional technologies for automating model generation are in PREVIEW state and currently only support Binary-Classification, Multiclass Classification and Regression. In upcoming versions we'll be supporting additional ML Tasks such as Recommendations, Anomaly Detection, Clustering, etc..
CLI samples: (Preview state)
The ML.NET CLI (command-line interface) is a tool you can run on any command-prompt (Windows, Mac or Linux) for generating good quality ML.NET models based on training datasets you provide. In addition, it also generates sample C# code to run/score that model plus the C# code that was used to create/train it so you can research what algorithm and settings it is using.
| CLI (Command Line Interface) samples |
|---|
| Binary Classification sample |
| MultiClass Classification sample |
| Regression sample |
AutoML API samples: (Preview state)
ML.NET AutoML API is basically a set of libraries packaged as a NuGet package you can use from your .NET code. AutoML eliminates the task of selecting different algorithms, hyperparameters. AutoML will intelligently generate many combinations of algorithms and hyperparameters and will find high quality models for you.
| AutoML API samples |
|---|
| Binary Classification sample |
| MultiClass Classification sample |
| Ranking sample |
| Regression sample |
| Advanced experiment sample |
Additional ML.NET Community Samples
In addition to the ML.NET samples provided by Microsoft, we're also highlighting samples created by the community showcased in this separated page:ML.NET Community Samples
Those Community Samples are not maintained by Microsoft but by their owners.If you have created any cool ML.NET sample, please, add its info into this REQUEST issue and we'll publish its information in the mentioned page, eventually.
Translations of Samples:
Learn more
See ML.NET Guide for detailed information on tutorials, ML basics, etc.
API reference
Check out the ML.NET API Reference to see the breadth of APIs available.
Contributing
We welcome contributions! Please review our contribution guide.
Community
Please join our community on Gitter
This project has adopted the code of conduct defined by the Contributor Covenant to clarify expected behavior in our community.For more information, see the .NET Foundation Code of Conduct.
License
ML.NET Samples are licensed under the MIT license.
-
机器学习 一、概述 1. 什么是机器学习？ 人工智能：通过人工的方法，实现或者近似实现某些需要人类智能处理的问题，都可以称为人工智能。 机器学习：一个计算机程序在完成任务T之后，获得经验E，而该经验的效果可以通过P得以表现，如果随着T的增加，借助P来表现的E也可以同步增进，则称这样的程序为机器学习系统。 特点：自我完善、自我修正、自我增强。 2. 为什么需要机器学习？ 简化或者替代人工方式的模式识
-
本文提出了交叉检验的框架，指的是在不同的数据集进行交叉验证。we endorse the idea of cross-evaluating ML-NIDS by using malicious samples captured in different network datasets.1 By performing such cross-evaluations, it is possible t
-
先秀一下本人机器学习资格认证证书，让大家可以放心Follow 关于MachineLearning这个项目说明 关于构建这个学习文档构建所依赖的工具 Atom --编辑软件 NodeJS --不安装的话，amWiki有可能装不上 amWiki --Atom的插件 Markdown --需要会基本语法 Mindjet --思维导图 GitHub
-
coursera-machinelearning Stanford University - Machine Learning by Andrew Ng Coursera: Machine Learning Summary Notes for quick review, based on the existing review part of this course. The Octave/MAT
-
NoteBooks Statistics and Machine Learning Notebooks as published on unsupervised-learning.com (RIP).
-
当我尝试在Visual Studio 2013上调试应用程序时引发此错误。 这是堆栈跟踪。 [UnauthorizedAccessException:对路径“D:\ Chart \ ASPNET CanvasJS Chart Samples \ ASPNET MVC Samples \ bin \ roslyn \ CSC . exe”的访问被拒绝。]system . io . _ _错误。Win
-
我尝试使用GitHub中的google speech client将语音转换为文本: https://github.com/GoogleLoudPlatform/java-docs-samples/tree/master/speech/cloud-client 在pom.xml中，它显示了以下问题: 描述资源路径位置类型插件执行不包括在生命周期配置中:org.apache.Maven.plugin
-
我尝试在balance transfer samples中注册用户，但其返回结果机密字段为空。我无法从rocket chat、google和stackoverflow中找到解决方案。有人能帮我吗？非常感谢。我执行这个代码“curl-s-X POST”http://localhost:4000/users-H“内容类型：应用程序/x-www-form-urlencoded”-d'用户名=Jim {成