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Add more automl and data-centric-AI tools #39

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Nov 15, 2022
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Add more automl and data-centric-AI tools #39

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6 changes: 6 additions & 0 deletions README.md
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* [TPOT](https://github.com/rhiever/tpot) - Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [auto-sklearn](https://github.com/automl/auto-sklearn) - An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
* [AutoGluon](https://github.com/awslabs/autogluon) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.

### Ensemble Methods
* [ML-Ensemble](http://ml-ensemble.com/) - High performance ensemble learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
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* [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
* [dopanda](https://github.com/dovpanda-dev/dovpanda) - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">

### Data-centric AI
* [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.
* [snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision.
* [dataprep](https://github.com/sfu-db/dataprep) - Collect, clean and visualization your data in python with a few lines of code.

## Feature Engineering

### General
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