Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 2h 47m

Genre: eLearning | Language: English | Size: 1.65 GBUse machine learning today without a machine learning background

Use machine learning today without a machine learning background

Key Features

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

About

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.