[dotnet-ai] ML.NET training and deployment

[luisquintanilla]

Fixes #229
Part of #225

Dependencies

Merge order: This PR should be merged after #237 Plugin scaffold.

Summary

Adds the mlnet skill covering the full ML.NET lifecycle: classical ML (classification, regression, clustering, anomaly detection, ranking, recommendation, forecasting) and deep learning via TorchSharp-backed trainers (image classification, object detection, NER, QA, text classification, sentence similarity).

Includes three reference files for deeper guidance on custom transforms, DataFrame for data preparation, and TorchSharp for custom neural network architectures.

Changes

File	Description
plugins/dotnet-ai/skills/mlnet/SKILL.md	Full ML.NET lifecycle: data loading, pipeline building, training, evaluation, deployment. Covers both classical ML and deep learning tasks
plugins/dotnet-ai/skills/mlnet/references/custom-transforms.md	4 approaches to building custom ML.NET pipeline transforms (IEstimator/ITransformer)
plugins/dotnet-ai/skills/mlnet/references/dataframe.md	Using Microsoft.Data.Analysis.DataFrame for data exploration and preparation
plugins/dotnet-ai/skills/mlnet/references/torchsharp.md	TorchSharp standalone for custom neural network architectures beyond ML.NET's built-in trainers
tests/dotnet-ai/mlnet/eval.yaml	Eval: ML.NET training scenario

Key Packages

• Microsoft.ML (core)
• Microsoft.ML.AutoML (AutoML)
• Microsoft.ML.TorchSharp (deep learning trainers)
• TorchSharp (standalone custom architectures)

Validation

• SKILL.md has valid YAML frontmatter (name, description)
• Covers classical ML + deep learning + AutoML + full lifecycle
• TF 2.3.1 version caveat included
• Three reference files for extended guidance
• eval.yaml has at least one scenario

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