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* [Colossal-AI](https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost. * [Colossal-AI](https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - facilitates machine-learning on dirty, non-curated categories. It provides transformers and encoders robust to morphological variants, such as typos. * [dirty_cat](https://github.com/dirty-cat/dirty_cat) - facilitates machine-learning on dirty, non-curated categories. It provides transformers and encoders robust to morphological variants, such as typos.
* [Upgini](https://github.com/upgini/upgini): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features. * [Upgini](https://github.com/upgini/upgini): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features.
* [AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics](https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics): A tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
<a name="python-data-analysis--data-visualization"></a> <a name="python-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization #### Data Analysis / Data Visualization