Updates from week of September 27, 2019

Sep 20, 2019

Using FeatureTools https://docs.featuretools.com/index.html + AutoML (H2O) http://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html
Tutorial from Analytics Vidhya https://www.analyticsvidhya.com/blog/2018/08/guide-automated-feature-engineering-featuretools-python/
AutoML: https://medium.com/@alxmamaev/how-to-build-automl-from-scratch-ce45a4b51e0f
ML-Workspace: https://github.com/ml-tooling/ml-workspace/
Kaggle Youtube-8m 2019 challenge https://www.kaggle.com/c/youtube8m-2019/overview
Kaggle Scripting Contest submissions: https://www.kaggle.com/general/109651
Pandas EDA: https://towardsdatascience.com/exploring-your-data-with-just-1-line-of-python-4b35ce21a82d
RAdam for tensorflow & keras https://pypi.org/project/tensorflow-radam/
Customer Lifetime Value: https://medium.com/@josh.temple/how-to-estimate-the-value-of-your-customers-the-right-way-57c63fad093
Lifetimes package: https://github.com/CamDavidsonPilon/lifetimes https://lifetimes.readthedocs.io/en/latest/Quickstart.html
Bruce Hardie: http://brucehardie.com/
Model Ensembling:
Stacking Made Easy http://blog.kaggle.com/2017/06/15/stacking-made-easy-an-introduction-to-stacknet-by-competitions-grandmaster-marios-michailidis-kazanova/
Kaggle Ensembling Guide https://mlwave.com/kaggle-ensembling-guide/
PyStackNet: https://github.com/h2oai/pystacknet
Intro to Stacking https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python
30 seconds of Python: https://github.com/30-seconds/30-seconds-of-python
Streamlit: https://towardsdatascience.com/coding-ml-tools-like-you-code-ml-models-ddba3357eace