Machine Learning Projects For .NET Developers
Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You will code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you are new to F#, this book will give you everything you need to get started. If you are already familiar with F#, this is your chance to put the language into action in an exciting new context.
Machine Learning Projects for .NET Developers
ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.
ML.NET has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more.
Using a 9GB Amazon review data set, ML.NET trained a sentiment analysis model with 95% accuracy. Other popular machine learning frameworks failed to process the dataset due to memory errors. Training on 10% of the data set, to let all the frameworks complete training, ML.NET demonstrated the highest speed and accuracy.
ML.NET is a free software machine learning library for C#, VB.NET, and F#. ML.NET includes transforms for feature engineering such as n-gram creation. ML.NET also includes transforms for learners to handle binary classification, multi-class classification, and regression tasks. ML.NET also includes anomaly detection and recommendation systems and deep learning.
ML.NET Builder is a UI tool from Microsoft that allows developers to train and build machine learning models in their applications. Model Builder supports AutoML. AutoML explores different machine learning algorithms automatically to assist in finding the correct algorithm that fits the current scenario.
ML.NET enables developers to use their existing .NET skills to easily integrate machine learning into almost any .NET application. This means that if C# (or F# or VB) is your programming language of choice, you no longer have to learn a new programming language, like Python or R, in order to develop your own ML models and infuse custom machine learning into your .NET apps. The framework offers tooling and features to help you easily build, train, and deploy high-quality custom machine learning models locally on your computer without requiring prior machine learning experience.
Although Microsoft announced ML.NET only two years ago, it was originally developed by Microsoft Research and has evolved into a significant machine learning framework that powers features in many Microsoft products, such as Microsoft Defender ATP, Bing Suggested Search, PowerPoint Design Ideas, Excel Chart Recommendations, and many Azure services.
Since ML.NET's launch, many companies have used the framework to add a variety of machine learning scenarios to their .NET apps, like Williams Mullen for law document classification, Evolution Software for hazelnut moisture level prediction, and SigParser for spam email detection.
Data Transforms: Because machine learning is all about math, all data needs to be converted to numbers or numeric vectors. ML.NET provides a variety of data transforms, such as text featurizers and one hot encoders, to convert your data to an acceptable format for the ML algorithms.
ClassicalMLtasks: ML.NET supports many classical machine learning scenarios and tasks, such as classification, regression, time series, and more. ML.NET provides more than 40 trainers (algorithms targeting a specific task), so you can select and fine-tune the specific algorithm that achieves higher accuracy and better solves your ML problem.
Model evaluation: Before using a trained model in production, you want to make sure it achieves the required quality when making predictions. ML.NET provides multiple evaluators related to each ML task so that you can find out the accuracy of your model, plus many more typical machine learning metrics depending on the targeted ML task.
With ML.NET, it takes only a few steps to build your own custom machine learning model. The code in Listing 1 demonstrates a simple ML.NET application that trains, evaluates, and consumes a regression model for predicting the price of taxi fare for a particular taxi ride.
Once you have your initial dataset configured to be used through an IDataView, you can use the IDataView like normal to perform the typical machine learning steps. You can check out a full sample app that reads data from a SQL Server database at -database-loader.
As mentioned above, machine learning algorithms generally can't directly use the data you have available for training; you need to use data transformations to pre-process the raw data and convert it into a format that the algorithm can accept.
Although writing the code to train ML.NET models is easy, choosing the correct data transformations and algorithms for your data and ML scenario can be a challenge, especially if you don't have a data science background. However, with the preview release of Automated Machine Learning and tooling for ML.NET, Microsoft has automated the model selection process for you so that you can easily get started with machine learning in .NET without requiring prior machine learning knowledge.
The Automated Machine Learning feature in ML.NET (in short called AutoML) works locally on your own development computer and automatically builds and trains models with a combination of the best performing algorithm and settings. You just have to specify the machine learning task and supply the dataset, and AutoML chooses and outputs the highest quality model by trying out multiple combinations of algorithms and related algorithm options.
Although you can use AutoML directly via the ML.NET AutoML API, ML.NET also offers tooling on top of AutoML to make machine learning in .NET even more approachable. In the next sections, you'll use the tools to see just how easy it is to create your first ML.NET model.
If you don't use Visual Studio or don't work on Windows, ML.NET also provides cross-platform tooling so that you can still use AutoML to easily create machine learning models. You can install and run the ML.NET CLI (command-line interface), a dotnet Global Tool, on any command-prompt (Windows, macOS, or Linux) to generate high-quality ML.NET models based on training datasets you provide. Like Model Builder, the ML.NET CLI also generates sample C# code to run that model plus the C# code that was used to create and train it so that you can explore the algorithm and settings that AutoML chose.
Learn about the machine learning scenarios supported by ML.NET in the ML.NET Samples GitHub repo at -mlnet-samples. You can also check out some samples written by the community or even contribute your own samples!
There are several ways that you can improve your machine learning model, including adding more training data, and training for a longer time. Check out -improve-model to learn more about how to improve the accuracy of your model.
Check out ML.NET Model Builder at -model-builder and learn how this UI tool in Visual Studio makes it even easier to get started with Machine Learning in .NET. You can upload a file or directly connect to SQL Server and build your custom machine learning model.
Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.
Azure Machine Learning designer: use the designer to train and deploy machine learning models without writing any code. Drag and drop datasets and components to create ML pipelines. Try out the designer tutorial.
In a repetitive, time-consuming process, in classical machine learning data scientists use prior experience and intuition to select the right data featurization and algorithm for training. Automated ML (AutoML) speeds this process and can be used through the studio UI or Python SDK.
Efficiency of training for deep learning and sometimes classical machine learning training jobs can be drastically improved via multinode distributed training. Azure Machine Learning compute clusters offer the latest GPU options.
Scaling a machine learning project may require scaling embarrassingly parallel model training. This pattern is common for scenarios like forecasting demand, where a model may be trained for many stores.
ML.NET is an open source and cross-platform machine learning framework built for .NET developers. With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you reuse all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps.
This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions.
.NET developers should have experience with Agile methodologies, which involve breaking up projects into smaller phases to plan, execute, and evaluate the software development process. This expert will likely use Behavioral Driven Development (BDD), DevOps tools, and Test Driven Development (TDD) to implement these methodologies. 041b061a72