This is an ever-evolving page where samples and content from the ML.NET community are highlighted, so anyone in the community can also take advantage of these additional samples.
However, note that Microsoft does not maintain the samples in the list below.
| Name | Description | ML Tasks or area of focus | API status | Owner |
|---|---|---|---|---|
| Inclusive Code Reviews | The goal of this project is to produce a machine learning model for classifying sentences for code reviews. | Binary Classification, Sentiment Analysis | Kudos for Jonathan Peppers (jonathanpeppers) and the DevDiv Inclusive Code Reviews v-team | |
| Malicious Packet Detection | Use anomaly detection with ML.NET to detect malicious network activity. | anomaly detection model | ML.NET v1.5.2 | Kudos for Mike Berg (mikekberg) |
| Silencer | A Chrome browser extension written in F# that scans your Twitter DMs and uses ML.NET to filter out toxic text. | classification model | ML.NET v1.5.2 | Kudos for Gregor Beyerle (WalternativE) |
| Smart Labeling | A tool that uses ML.NET to create labeled datasets. | image classification model | ML.NET v1.5.2 | Kudos for Daniel Costea (dcostea) |
| PhotoBombers | An automated object tagging utility for photos using ML.NET | image classification model | ML.NET v1.5.2 | Kudos for Paul Amazona (whatevergeek) |
| Stonks Inc! | Use ML.NET to predict future stock prices. | forecasting model | ML.NET v1.5.2 | Kudos for Joey Schentrup (joeySchentrup) |
| Wine Lovers | Use ML.NET to suggest a type of wine based on a free-form description. | classification model | ML.NET v1.5.0 | Kudos for Mircea Dogaru (M1rceaDogaru) |
| Predicting System Load | Use ML.NET to predict load of withdrawal service of a bank for a given day. | forecasting model | ML.NET v1.5.2 | Kudos for Sumeet More (sumbagaara) and Rohan Ghodke |
| Real-Time Object Detection | Use ML.NET apply object detection to real-time web cam footage | object detection model | ML.NET v1.5.2 | Kudos for Sam (msadengineer) |
| Visual Inspection & Classification of X-Ray Chest Disease | Use ML.NET to visually inspect a sample of X-Ray chest images and predict the sample image according to the categories of Chest Disease | image classification model | ML.NET v1.4 | Kudos for Yinka Olowofela (olowoyinka) |
| COVID-19 Exploratory Data Analysis using .NET | Use .NET DataFrame API and ML.NET to perform analysis on COVID-19 dataset and visualize the trends of virus spread in various countries | forecasting model | ML.NET v1.5 | Kudos for Praveen Raghuvanshi (praveenraghuvanshi) |
| Rom-com or not rom-com | ML.NET project designed to run a machine learning model over a text document and determine if it exhibits the classic characteristics of a rom-com film script. | multi-class classification model | ML.NET v1.4 | Kudos for David Gristwood (davidgristwood) |
| Photo-Search | Sample WPF app running an ONNX model which was previously built with Keras and exported to ONNX model format. | Deep Learning, Image classification | ML.NET v0.9 and .NET Core 3.0 | Kudos for Tak-Au |
| ML.NET sample running on 'TRY .NET' | This sample is compatible with Try .NET. | Binary Classification | Updated to ML.NET v1.2 | .NET team |
| ML.NET Custom Transform | Show a custom transform implementation in ML.NET | Transform | Updated to ML.NET v1.2 | Kudos for endintiers |
| Additional ML.NET Demos | Multiple additional ML.NET demos/samples | Multiple ML tasks | ML.NET v1.0 | Kudos for jeffprosise |
| VB.NET samples for ML.NET | VB.NET samples, similar to some of the Getting Started samples, but implemented with VB.NET | Most in ML.NET | Updated to ML.NET v0.9 | Kudos for Nukepayload2 |
| ML.NET in visual desktop UWP app | Demonstrates how to use ML.NET to implement some Machine Learning use cases in UWP. Blog Post here | Multiple ML tasks | 1.0.0 | Kudos for @diederikkrols |
| Multi-Output Regression with ML.NET & TensorFlow | Multi-Output Regression with ML.Net and TensorFlow | Regression with TF | Updated to ML.NET v0.9 | Kudos for zeahmed |
| DataFrame for ML.NET | It implements a subsample of pandas's dataframes API, on top of ML.NET | Data wrangling | TBD | Xavier Dupré xadupre [MSFT] |
| Beer-ML | Demo project uses Systembolaget (government owned chain of liquor stores in Sweden) database for four types of ML Tasks | Binary Classification, Multi-Class Classification, Regression, Clustering | ML.NET v1.0.0-preview | Alexander Dragunov, adrag239 |
| Simple Linear Regression | Predicts salary based off years of experience. | Regression | Uses Static API. Needs to migrate to Dynamic API | Jon Wood, jwood803 |
| ONNX model scoring | Apache MXNet MLP model exported to ONNX and used in ML.NET | Regression | Cosmin Catalin, cosmincatalin | |
| IRIS Flowers Classifier - UWP APP | IRIS Flower Species Prediction | Multiclass Classification | ML.Net 1.0.0 | Paula Scholz,PaulaScholz |
| Taco vs Burrito Image Classifier | Taco vs Burrito Image Classifier | Image Classification using TensorFlow -Transfer Learning | 1.0.0 | Seth Juarez,sethjuarez |
| Multiple ML.NET samples and demos | Multiple ML.NET samples and demos | TensorFlow, AutoML, classification, Digit classification, etc. | 1.0.0 | by jeffprosise |
| Mushroom Classifier Using C# and ML.Net | App to classify the mushrooms whether they are edible or poisonouss | Binary Classification | 1.3.1 | by deepak21188 |
Chinese Samples
| Name | Description | ML Tasks or area of focus | API status | Owner |
|---|---|---|---|---|
| Chemical Molecular Data Format | Demonstrates how to use the ML.NET to predict chemical molecular data format. | Multi-Class classification | ML.NET v0.10 and .NET framework 4.6.1 | Kudos for Chen Qing Yang |
| Other ML.Net Samples | Consists of list of ML.NET samples in Chinese similar to ML.NET Samples in English | ML Tasks similar to taks in ML.NET Samples | ML.NET v0.7 | Kudos for feiyun0112 |
Videos
| Name | Description | Owner |
|---|---|---|
| ML, Data Science, AI Playlist | YouTube playlist containing an introduction and sample walkthroughs of ML.NET | Kaushik Roy Chowdhury, krchome |
Do you have any cool ML.NET sample that you'd like to share with the community? If so, add it to the following issue and eventually, it'll be showcased in this page.
Thanks for your contribution! :)