josephmisiti/awesome-machine-learning
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Merge pull request #909 from jwmueller/master

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Add some of my favorite tools
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* [IoT Owl](https://github.com/Ret2Me/IoT-Owl) - Light face detection and recognition system with huge possibilities, based on Microsoft Face API and TensorFlow made for small IoT devices like raspberry pi.
* [Exadel CompreFace](https://github.com/exadel-inc/CompreFace) - face recognition system that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition and is easily deployed with docker.
* [computer-vision-in-action](https://github.com/Charmve/computer-vision-in-action) - as known as ``L0CV``, is a new generation of computer vision open source online learning media, a cross-platform interactive learning framework integrating graphics, source code and HTML. the L0CV ecosystem — Notebook, Datasets, Source Code, and from Diving-in to Advanced — as well as the L0CV Hub.
* [timm](https://github.com/rwightman/pytorch-image-models) - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
<a name="python-natural-language-processing"></a>
#### Natural Language Processing
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* [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.
* [SKBEL](https://github.com/robinthibaut/skbel): A Python library for Bayesian Evidential Learning (BEL) in order to estimate the uncertainty of a prediction.
* [NannyML](https://bit.ly/nannyml-github-machinelearning): Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
* [NannyML](https://bit.ly/nannyml-github-machinelearning): Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
* [cleanlab](https://github.com/cleanlab/cleanlab): The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
* [AutoGluon](https://github.com/awslabs/autogluon): AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
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#### Data Analysis / Data Visualization