Udemy course Introduction to ML.NET or Machine Learning with .NET by Harshit Gindra
Introduction to ML.NET or Machine Learning with .NET is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Introduction to ML.NET or Machine Learning with .NET udemy course.
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Course data:
- Author: Harshit Gindra
- Course rating: 3.6
- Category: Development
- Modality: Online
- Status: Available
- Idiom: English
Abouth Harshit Gindra
Full stack developer in Microsoft technologies especially ASP.NET, Xamarin Forms and Azure Cloud Services. Have been working on ASP.NET projects for the last 5 years and developed several Xamarin forms mobile applications as my hobby. My interest lies in learning and gaining hands on experience of some of latest technologies releasing everyday
What the udemy Introduction to ML.NET or Machine Learning with .NET course teaches?
What you’ll learn Understand what is Machine Learning Machine Learning basic concepts Machine Learning with .Net technologies ML .Net Create Machine Learning Models Machine Learning Algorithms Basic understanding about Data Science How Machine Learning works with examples Creating machine learning models using .net technologies
Understand Machine Learning and how to use it with .NET and C# in a real world application using different examples
More information about the course Introduction to ML.NET or Machine Learning with .NET
This course will help students understand what is Machine Learning, the process involved in Machine Learning and how we can do Machine Learning using .NET technologies or Libraries. This course throws light on some of the Machine Learning concepts, its applications, steps involved in building models and consuming those models using C#, .NET and Visual Studio. This course explains the steps involved in building models using different examples and different algorithms suited for each of the scenarios.
