.Net Foundation / ML.NET

How to use ML.NET in an Event Hub consumer to get real-time data predictions.

C# Consumer code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/EventHubPredict/Program.cs

Python notebook producer code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/Notebooks/ML.NET%20Event%20Hub%20Producer.ipynb

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

Contact:
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How to produce n-grams from text data in ML.NET.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/NGrams/Program.cs

N-Gram article - https://blog.xrds.acm.org/2017/10/introduction-n-grams-need/

ML.NET NLP Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZzYBP8I7l9MDQY3r6HbVxWw

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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A quick video to show the new models you can build with the updated ML.NET Model Builder.

Announcement blog post - https://devblogs.microsoft.com/dotnet/ml-net-model-builder-updates/

Model Builder introduction video - https://www.youtube.com/watch?v=2M75v9z9R2U

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

Contact:
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Using ML.NET within the Microsoft Bot Framework to make predictions on a machine learning model.

Code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/PredictionBot

Bot Framework playlist - https://www.youtube.com/playlist?list=PLl_upHIj19Zy6hEcOa-uLC0Ged32or2XZ

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

Contact:
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How to use ML.NET to create predictions, or forecasts, on your time series data.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/TimeSeriesForecast/Program.cs

Detect anomalies in time series data in ML.NET - https://www.youtube.com/watch?v=Nu9lz5jNkGs
Detect anomalies with Cognitive Services - https://www.youtube.com/watch?v=gfb63wvjnYQ

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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This video shows how to use your previously saved model to retrain a new model with new data.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/RetrainModel/Program.cs

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Learn how to find and replace missing values in your data in ML.NET.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/NullValues/Program.cs

ML.NET Transforms Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZyJf-ItL5Mey9mniPiTKtvv

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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How to use a model from the Custom Vision service in ML.NET to make predictions.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/CustomVisionOnnx/Program.cs

Netron - https://github.com/lutzroeder/netron

Custom Vision Sample - https://github.com/dotnet/machinelearning-samples/tree/master/samples/csharp/end-to-end-apps/ObjectDetection-Onnx

Custom Vision model video - https://www.youtube.com/watch?v=zr6M7Lzr48w&t=28s

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Showing how to use the new Expression Transform in ML.NET.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/ExpressionTransform/Program.cs

Expression Transform Documentation - https://github.com/dotnet/machinelearning/blob/master/docs/api-reference/expression-estimator.md

ML.NET Transforms Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZyJf-ItL5Mey9mniPiTKtvv

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Update on an old video that creates a linear regression machine learning pipeline using the latest version of ML.NET (v1.4).

Original video - https://www.youtube.com/watch?v=8gVhJKszzzI

Code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/SimpleRegressionUpdate

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Using C# within a Jupyter notebook was just announced at Ignite. In this video, we will go over how you can do this and perform an AutoML experiment.

Notebook - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/Notebooks/Dataframe%20with%20AutoML.ipynb

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

ML.NET Jupyter Notebooks Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZyPO7jwtydz9dPW7AS6x7uw&playnext=1&index=1

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This video will show how to scale numerical data using ML.NET to help you get better performance out of your machine learning algorithms.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/ScaleData/Program.cs

ML.NET Transforms playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZyJf-ItL5Mey9mniPiTKtvv

ML.NET Playlist - https://www.youtube.com/watch?v=8gVhJKszzzI&list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Using ML.NET to remove stop words in text data.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/StopWords/Program.cs

ML.NET NLP Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19ZzYBP8I7l9MDQY3r6HbVxWw

ML.NET Playlist - https://www.youtube.com/playlist?list=PLl_upHIj19Zy3o09oICOutbNfXj332czx

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Announcing .NET Core 3.0: https://aka.ms/dotnetcore3
#dotNETConf

Get started with ML.NET so you can create machine learning models in C# to run impressive predictions and use them in your .NET applications!

Create innovation in your apps with scenarios such as sentiment analysis, anomaly detection, text classification, object detection and many more!

In the world of ML.NET there is a high-volume of interaction with GitHub. This video will teach you how to classify incoming GitHub issues into one of the many tags (labels) using a multi-class classification algorithm.

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Multiclass classification with ML.NET: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/github-issue-classification?&WT.mc_id=Educationalmlnet-c9-niner
What is Model Builder and how does it work? https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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Learn .NET: https://dotnet.microsoft.com/learn?&WT.mc_id=Educationalmlnet-c9-niner
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.NET Documentation: https://docs.microsoft.com/en-us/dotnet/?&WT.mc_id=Educationalmlnet-c9-niner

Following our last video, here are even more ways to use the model builder with ML.NET. In this video, we learn how to recommend movies for users using colloborative filtering based recommendation approach.

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Movie recommendation with ML.NET: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/movie-recommendation?&WT.mc_id=Educationalmlnet-c9-niner
What is Model Builder and how does it work? https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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In this video, we will cover how to build a ML model for sentiment analysis of customer reviews using a binary classification algorithm.

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Binary Classification in ML.NET: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis?&WT.mc_id=Educationalmlnet-c9-niner
What is Model Builder and how does it work? https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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.NET Documentation: https://docs.microsoft.com/en-us/dotnet/?&WT.mc_id=Educationalmlnet-c9-niner

There are tons of different ways to use the model builder with ML.NET. In this video, we learn how to predict taxi fares based on distance traveled, trip time etc. using a regression algorithm.

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Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Predict prices with ML.NET: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
What is Model Builder and how does it work? https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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Deep learning enables many more scenarios using sound, images, text and other data types. Learn how to build an Image Classification model to classify flowers (daisies, roses etc.).

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Image classification with ML.NET: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/image-classification?&WT.mc_id=Educationalmlnet-c9-niner
What is Model Builder and how does it work? https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-model-builder?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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.NET Documentation: https://docs.microsoft.com/en-us/dotnet/?&WT.mc_id=Educationalmlnet-c9-niner

Learn how you can get started with ML.NET on Windows, Mac, and Linux using tools (ML.NET Model Builder, ML.NET CLI) or code-first using ML.NET API.

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
What is Machine Learning with .NET? https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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.NET Documentation: https://docs.microsoft.com/en-us/dotnet/?&WT.mc_id=Educationalmlnet-c9-niner

Learn all about what ML.NET is and what you can do with the Framework. You will also see how customers using it, the open source momentum, and a short demo of the experience.

Follow Pranav Rastogi: https://twitter.com/rustd

Watch the entire series here: https://aka.ms/dotnet3-mlnet
Download Visual Studio 2019 here: https://aka.ms/VisualStudio-dotnetconf19
Download .NET Core 3.0 here: https://aka.ms/Dotnet3

Learn more about ML .NET:
What is Machine Learning with .NET? https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet?&WT.mc_id=Educationalmlnet-c9-niner
Get started with ML.NET: https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro?&WT.mc_id=Educationalmlnet-c9-niner
ML.NET Model Builder: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet/model-builder?&WT.mc_id=Educationalmlnet-c9-niner
GitHub Repo ML .NET: https://github.com/dotnet/machinelearning
GitHub Repo ML.NET Samples: https://github.com/dotnet/machinelearning-samples/blob/master/README.md

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.NET Documentation: https://docs.microsoft.com/en-us/dotnet/?&WT.mc_id=Educationalmlnet-c9-niner

Using ML.NET to tokenize text data to get it ready for machine learning algorithms.

This video shows how to save and load in the model for the deep neural network to classify sushi vs. sandwich images.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/PredictDNN/Program.cs

Video that creates the DNN model - https://www.youtube.com/watch?v=bXTN-rnwDso

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The new DatabaseLoader in ML.NET was updated in the 1.4-preview version. This video goes over how to use it.

Data - https://www.kaggle.com/camnugent/california-housing-prices

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/DatabaseLoader/Program.cs

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Update of this using the Microsoft.ML.Vision package is here - https://youtu.be/ppRauvf6uCs

Use ML.NET and the (currently) preview version of 1.4 to create a deep neural network model to classify images.

Data - https://www.kaggle.com/brtknr/sushisandwich

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/DeepNeuralNetwork/Program.cs

Transfer learning video - https://www.youtube.com/watch?v=tAiKAtmPaXU

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Learn how to use ML.NET to perform transfer leaning from the Inception model built in Tensorflow.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/TransferLearning/Program.cs

Doc - https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/image-classification

Inception model download - https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

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Data preparation is important to get your data in a workable state for it to work in a machine learning algorithm. This video will show how to select columns, drop columns, take rows, shuffle rows, and filter rows in ML.NET to start preparing your data.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/DataPrepRowsColumns/Program.cs

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Gear used (affiliate links):
Mic - https://amzn.to/2YEXtxI
Mouse - https://amzn.to/2ZtASoQ

Using ML.NET in an Azure Function with an HTTP Trigger to perform predictions on a previously build ML.NET model.

Code that saves the model file - https://github.com/jwood803/MLNET-HousingRegression/tree/master/HouseRegression/HouseRegression

Azure function code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/AzureFunction

ML.NET Web API video - https://www.youtube.com/watch?v=FFvpEeWW7AQ

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Gear used (affiliate links):
Mic - https://amzn.to/2YEXtxI
Mouse - https://amzn.to/2ZtASoQ

Using F# to build a machine learning model with ML.NET.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/FSharpRegression/Program.fs

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Gear used (affiliate links):
Mic - https://amzn.to/2YEXtxI
Mouse - https://amzn.to/2ZtASoQ

Jeff wants to learn about Machine Learning and starts going through the ML.NET tutorial with an intended goal of writing a feature that detects the hat he is wearing on stream and reports it in chat.

He doesn't get very far with his goal feature, but learns a lot about machine learning along the way -- Watch live at https://www.twitch.tv/csharpfritz

Using a ML.NET transform to detect anomalies in time series data.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/AnomalyDetection/Program.cs

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Create new columns in your data for feature engineering in ML.NET by creating a custom transform.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/CustomTransform/Program.cs

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

This video goes over ONNX and how to read and write an ONNX model using ML.NET.

Code - https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/Onnx/Program.cs

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

A quick tour of using the ML.NET CLI to explore machine learning models with the AutoML engine.

Data - https://www.kaggle.com/camnugent/california-housing-prices

CLI Documentation - https://docs.microsoft.com/en-us/dotnet/machine-learning/reference/ml-net-cli-reference

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Using the AutoML package to find a best ML.NET model for the given data.

Code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/AutoML

Data - https://www.kaggle.com/camnugent/california-housing-prices

AutoML documentation - https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api#load-data

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Continuing the end-to-end ML.NET project by creating a Web API that loads the ML.NET model and gives a prediction.

First video in series - https://www.youtube.com/watch?v=VVekyUMEKsI

Code - https://github.com/jwood803/MLNet-EndToEnd/tree/master/WineAPI

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

ML.NET is a free, cross-platform, and open source machine learning framework for .NET developers. It is also an extensible platform that powers Microsoft services like Windows Hello, Bing Ads, PowerPoint Design Ideas, and more. This session focuses on the release of ML.NET 1.0. If you want to learn the basics about machine learning and how to develop and integrate custom machine learning models into your applications, this demo-rich session is made for you!

ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.

Live from Build 2019, we are joined by Cesar De La Torre Llorente who gives us a great overview of what the goals of ML.NET are, and shares with us some of the highlights of the 1.0 release.


[00:41] - Machine Learning vs Artificial Intelligence
[01:30] - What is a Machine Learning model?
[02:15] - Why did we create ML.NET
[04:27] - Can ML.NET both train and consume models?
[05:23] - How can Machine Learning help my application?
[07:04] - How can you get started with ML.NET
[10:21] - Using the ML.NET CLI
[12:41] - How do you prepare your data for training?
[15:09] - Generating code using the ML.NET Visual Studio extension
[15:48] - Where can you run ML.NET?


Announcing ML.NET 1.0
https://devblogs.microsoft.com/dotnet/announcing-ml-net-1-0/?WT.mc_id=ondotnet-c9-cxa

ML.NET Documentation
https://docs.microsoft.com/en-us/dotnet/machine-learning?WT.mc_id=ondotnet-c9-cxa

The first in a series of videos that goes end-to-end with an ML.NET model. This video goes over creating and using data in a database to create a model to predict wine quality and saves the data into Azure Blob Storage.

End-to-end 2: Web API video - https://www.youtube.com/watch?v=FFvpEeWW7AQ

Code - https://github.com/jwood803/MLNet-EndToEnd/blob/master/WineRegressionModel/Program.cs

Data - https://www.kaggle.com/rajyellow46/wine-quality

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Video to show how to perform the clustering machine learning algorithm with K-Means in ML.NET.

Text version of this sample - https://jonwood.co/blog/2019/3/2/clustering-in-mlnet

Data used - https://www.kaggle.com/dongeorge/seed-from-uci

Sample code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/SeedClustering

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/

Quick video to show how to create a recommendation model with the Matrix Factorization trainer using the GoodReads book rating data set.

Data - https://www.kaggle.com/dicoderdisha/goodreads-dataset
Matrix factorization video - https://www.youtube.com/watch?v=ZspR5PZemcs

Full sample code - https://github.com/jwood803/MLNetExamples/tree/master/MLNetExamples/BookRecommendations

Contact:
Twitter: https://twitter.com/JWood/
Blog: https://jonwood.co/