Hands-On Machine Learning for .NET Developers

Rating 4.41 out of 5 (73 ratings in Udemy)

What you'll 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 …

Rating 4.41 out of 5 (73 ratings in Udemy)

What you'll 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

Description

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.

About the Author

Karl Tillström has been passionate about making computers do amazing things ever since childhood and is strongly driven by the magic possibilities you can create using programming. This makes advances in machine learning and AI his holy grail; since he took his first class in artificial neural networks in 2007, he has experimented with machine learning by building all sorts of things, ranging from Bitcoin price prediction to self-learning Gomoku playing AI.

Karl is a software engineer and systems architect with over 15 years' professional experience in .Net, building a wide variety of systems ranging from airline mobile check-ins to online payment systems.

Driven by his passion, he took a Master's degree in Computer Science and Engineering at the Chalmers University of Technology, a top university in Sweden.

Duration 2 Hours 58 Minutes

Paid

Self paced

Beginner Level

English (US)

512

Rating 4.41 out of 5 (73 ratings in Udemy)

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