How to: Machine Learning
_Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python)._

- [diffusers](https://github.com/huggingface/diffusers) - A library that provides pretrained diffusion models for generating and editing images, audio, and video.
- [gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.
- [Feature-engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection.
- [H2O](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.
- [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics.
- [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.
- [PraisonAI](https://github.com/MervinPraison/PraisonAI) - Production-ready Multi-AI Agents framework with self-reflection, 100+ LLM support, MCP integration, and agentic workflows.
- [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.
- [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.
- [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.