Tags: .NET, Algorithms, applications, Code, developers, Karl Tillström, Machine Learning, ML.NET, ONNX, Tensorflow
By Karl Tillström
Use machine learning today without a machine learning background
- Quickly get up and running using state-of-the-art machine learning algorithms in your .Net applications
- Implement machine learning algorithms using real-world data sets, without first learning math
- Leverage state-of-the-art (TensorFlow, ONNX) models, pre-trained by the tech giants, in your own .Net code
What You Will Learn
- Quickly implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP.Net Web.APIs, desktop applications, and Dotnet core console apps
- Use the advances in machine learning with models customized to your needs
- Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools
- Improve and retrain your models for better performance and accuracy
- Basic overview of machine learning through a hands-on approach
- Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting
- Data loading and preparation for model training
- Leverage state of the art TensorFlow and ONNX models directly in .NET
ML.NET enables developers utilize their .NET skills to easily integrate machine learning into virtually any .NET application. This course will teach you how to implement machine learning and build models using Microsoft’s new Machine Learning library, ML.NET. You will learn how to leverage the library effectively to build and integrate machine learning into your .NET applications.
By taking this course, you will learn how to implement various machine learning tasks and algorithms using the ML.NET library, and use the Model Builder and CLI to build custom models using AutoML.
You will load and prepare data to train and evaluate a model; make predictions with a trained model; and, crucially, retrain it. You will cover image classification, sentiment analysis, recommendation engines, and more! You’ll also work through techniques to improve model performance and accuracy, and extend ML.NET by leveraging pre-trained TensorFlow models using transfer learning in your ML.NET application and some advanced techniques.
By the end of the course, even if you previously lacked existing machine learning knowledge, you will be confident enough to perform machine learning tasks and build custom ML models using the ML.NET library.
All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-.NET-Developers-V
About the Author(s)