AI News, dotnet/machinelearning

dotnet/machinelearning

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using: or from the NuGet package manager: Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework

Today at //Build 2018, we are excited to announce the preview of ML.NET, a cross-platform, open source machine learning framework.

ML.NET will allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models.

Along with these ML capabilities, this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework, such as learning algorithms, transforms, and core ML data structures.

Please come and join us over on GitHub and help shape the future of ML in .NET: https://github.com/dotnet/machinelearning Over time, ML.NET will enable other ML scenarios like recommendation systems, anomaly detection, and other approaches, like deep learning, by leveraging popular deep learning libraries like TensorFlow, Caffe2, and CNTK, and general machine learning libraries like Accord.NET.

Nagesh Pabbisetty ML.NET is being launched as a part of the .NET Foundation and the repo today contains the .NET C# API(s) for both model training and consumption, along with a variety of transforms and learners required for many popular ML tasks like regression and classification.

ML.NET comes with support for the types and runtime needed for all aspects of machine learning, including core data types, extensible pipelines, high performance math, data structures for heterogeneous data, tooling support, and more.

We then apply a TextFeaturizer to convert the SentimentText column into a numeric vector called Features which can be used by the machine learning algorithm (as it cannot take text input).

.Net Developers can Write Machine Learning Code Too: The Case for and Against ML.NET

If you are working on machine learning projects in the real world you live and die by Python.

Over the years, the Python ecosystem has slowly building a rich assembly of frameworks, research and tools that makes it the favorite destination for data scientists.

Microsoft has been slowly complementing its impressive machine learning infrastructure platform with frameworks and libraries that make machine learning/machine learning more accessible to .Net developers.

The first step to get started with ML.NET is to install the ML.NET NuGet package using the following code: After that, we can create an instance of the LearningPipeline class which is the main component used for data loading and featurization of a machine learning model.

For instance, loading data from a text file can be easily accomplished as follows: In most machine learning scenarios, data needs to be pre-processed and cleaned.

However, like many of other “bridge machine learning frameworks” we should setup the right constraints for the use of ML.NET ML.NET is a fantastic vehicle for .Net developers to get started implementing basic machine learning applications.

The idea ML.NET is meant to be used for implementing basic machine learning is challenged by the fact that there is no clear programming model to transition to more sophisticated stacks such as the Cognitive Toolkit.

First Look of ML.NET: Microsoft Machine Learning Framework for .Net

I always wanted to have a NuGet package that could be plugged with a .Net application by which we can create Machine learning applications.

Along with some basic algorithms, we can train the model and predict using models, along with other basic Machine Learning tasks.

Apart from this, ML.NET also supports core data types, extensible pipelines, high-performance math, data structures for heterogeneous data, tooling support, and more.

We will create a LearningPipeline which will encapsulate the data loading, data processing/featurization, and learning algorithm: Imagine we have a big file with all the data we require and we want to apply ML algorithms to that data.

We get the path as below: Once we have the path, use TextLoader to load the data from training file: pipeline.Add(new TextLoader<SentimentData>(dataPath, useHeader: true, separator: “tab“));

The next, and important, step is to train your pipeline, which basically loads the data and trains the featurizer and learner: var model = pipeline.Train<SentimentData, SentimentPrediction>();

Below is the code for prediction: Note – Code reference and whole code is here: https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs Get more details from here:https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet That is it.

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