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docs: enhance ML library descriptions with additional context #2888

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Expand Up @@ -736,10 +736,10 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git
- [pydantic-ai](https://github.com/pydantic/pydantic-ai) - A Python agent framework for building generative AI applications with structured schemas.
- [RAGFlow](https://github.com/infiniflow/ragflow) - An open-source RAG engine for document understanding and question answering with LLMs.
- [rasa](https://github.com/RasaHQ/rasa) - An open-source machine learning framework for automated text and voice-based conversations.
- [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning.
- [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library.
- [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning with extensive documentation and community support.
- [Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library for distributed computing.
- [Transformers](https://github.com/huggingface/transformers) - A framework that lets you easily use pretrained transformer models for NLP, vision, and audio tasks.
- [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.
- [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library for efficient ML.

## Microsoft Windows

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