Getting Started with ML.NET: Build a Sentiment Analysis Model

Integrating machine learning into your .NET applications doesn't require a degree in data science. With ML.NET, C# developers can easily build and deploy custom machine learning models using familiar tools and languages. This text-based course guides you through the foundational concepts of machine learning before walking you step-by-step through building a practical sentiment analysis model. You will transition from understanding core ML terminology to training a classification model that analyzes text reviews using command-line tools and C#. What you'll learn: - Understand foundational machine learning concepts, pipelines, and terminology within the .NET ecosystem. - Prepare and clean text datasets for training classification models. - Configure and use the ML.NET CLI to automate model selection and training. - Evaluate model performance using key metrics like accuracy and F1 score. - Integrate the trained model into a modern .NET application using dependency injection. - Practice writing C# code to make real-time sentiment predictions on new text inputs. You will start with the absolute basics of data preparation and ML pipelines, then progress to hands-on model training using command-line workflows, and conclude by writing C# code to consume your newly created model. This course is designed for .NET developers and beginners who want to learn machine learning without needing prior experience in Python or complex data science frameworks. Start reading today to add machine learning capabilities to your .NET development toolkit.