Machine Learning with ML.NET: Two Practical Series

Over the past few weeks, I've published two article series focused on applying Machine Learning with C# and ML.NET to real-world problems. The first series explores how machine learning models can be used to detect vulnerabilities in source code, covering everything from the fundamentals to modern Transformer-based architectures. The second series walks through the development of a music recommendation system, including model training, ranking-based evaluation, and deployment through an ASP.NET Core Web API. The goal is to provide practical content that combines machine learning concepts, implementation, and model evaluation for developers who want to learn ML within the .NET ecosystem.

📚 Series 1 - Source Code Vulnerability Detection

Teaching Machines to Learn https://devfullstack.net/blog/teaching-machines-to-learn

From Counting Words to Transformers https://devfullstack.net/blog/from-counting-words-to-transformers

What Vulnerable Code Is and Why It Goes Unnoticed https://devfullstack.net/blog/what-vulnerable-code-is-and-why-it-goes-unnoticed

CodeBERT and the AIs That Read Code https://devfullstack.net/blog/codebert-and-the-ais-that-read-code

Building a Vulnerable Code Detector with TorchSharp https://devfullstack.net/blog/vulnerable-code-detector-with-torchsharp

Building a Transformer from Scratch https://devfullstack.net/blog/building-a-transformer-from-scratch

🎸 Series 2 - Music Recommendation System

How Recommendation Systems Work https://devfullstack.net/blog/how-recommendation-systems-work

How to Evaluate a Recommendation Model https://devfullstack.net/blog/how-to-evaluate-a-recommendation-model

Training a Rock Recommender with ML.NET https://devfullstack.net/blog/training-a-rock-recommender-with-mlnet

Evaluating the Rock Recommender https://devfullstack.net/blog/evaluating-the-rock-recommender

Building the Rock Recommender API https://devfullstack.net/blog/building-the-rock-recommender-api