# Additional Resources

## Documentation

* AI fairness 360
  * [GitHub](https://github.com/Trusted-AI/AIF360)
  * [Demo](https://aif360.mybluemix.net)
  * [API documentation](https://aif360.readthedocs.io/en/latest/)
* AI explainability 360
  * [GitHub](https://github.com/Trusted-AI/AIX360)
  * [Demo](http://aix360.mybluemix.net)
  * [API documentation](https://aix360.readthedocs.io/en/latest/)

## Learn more

At [our UK meetup](https://www.meetup.com/IBM-Code-London) group where we host weekly workshops

From our [developer site](https://developer.ibm.com/) where you can find tutorials, code examples, articles and more

At our [CODE@THINK](https://www.ibm.com/uk-en/events/think-summit/) event on 7 October

Or through these resources (more will be added):

* [Data analysis with Python](https://developer.ibm.com/technologies/data-science/series/learning-path-data-analysis-using-python/)
* [Machine learning for developers](https://developer.ibm.com/series/learning-path-machine-learning-for-developers)
* [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)

## Contact me

* On [twitter](https://twitter.com/MargrietGr)
* On [linkedIn ](https://www.linkedin.com/in/margrietgroenendijk/)
