Ultimate Guide to Machine Learning with ML.NET

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Code that accompanies this article can be downloaded here. Several months ago I wrote a series of articles about ML.NET. Back then ML.NET was at its infancy and I used 0.3 version to solve some real-world problems. One of my examples even ended up in the official ML.NET GitHub. However, guys at Microsoft decided to change the whole ML.NET API in version 0.6 making my articles somewhat outdated. Apart from that, we had two previews of .NET Core 3 in the last two months, which means it will be out soon-ish. ML.NET should be a part of .NET Core 3 release, so my assumption is that there won't be any ground-breaking changes in the API once again. This is why I decided to write another article on this topic and cover all the things once again, but using the new API. To be honest, changing from 0.3 ML.NET version to 0.10 ML.NET version (latest version) were not as straight-forward as I hoped so. Some of the things I don't like so much, others I adored.


ML.NET: Machine Learning for .NET Developers

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Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.


ML.NET: Machine Learning for .NET Developers

#artificialintelligence

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.


Machine Learning with ML.NET in UWP: Clustering

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This is the first in a series of articles on implementing Machine Learning scenarios in UWP apps. All of these are cross platform Open Source technologies, all of these are written in C#, all of these are free, and all of these can be used on the UWP platform, albeit with some -hopefully temporary- restrictions. Currently the large majority of the online samples on ML.NET are straightforward console apps. That's fine if want to learn the API, but we want to figure out how ML.NET behaves in a more hostile enterprise-ish environment – where calculations should not block the UI, data should be visualized in sexy graphs, and architectural constraints may apply. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends.


Getting Started with Machine Learning DotNet (ML.NET)

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In Build 2018, Microsoft introduced the preview of ML.NET (Machine Learning .NET) which is a cross platform, open source machine learning framework. Yes, now it's easy to develop our own Machine Learning application or develop a custom module using Machine Learning framework. ML.NET is a machine learning framework which was mainly developed for .NET developers. We can use C# or F# to develop ML.NET applications. ML.NET is an open source which can be run on Windows, Linux and macOS.