Further reading
For more information on the topics that were covered in this chapter, please refer to the resources and links:
- TPOT for Automated ML in Python:https://machinelearningmastery.com/tpot-for-automated-machine-learning-in-python/
- Featuretools Demos:https://www.featuretools.com/demos/
- Boston Dataset:https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html
- How to Automate ML: https://www.knime.com/blog/how-to-automate-machine-learning
- Data-driven advice for applying ML to bioinformatics problems, by Randal S. Olson:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890912/
- TPOT Automated ML in Python: https://towardsdatascience.com/tpot-automated-machine-learning-in-python-4c063b3e5de9
- Microsoft NNI: https://github.com/microsoft/nni
- auto-sklearn:https://automl.github.io/auto-sklearn/master/examples/20_basic/example_regression.html#sphx-glr-examples-20-basic-example-regression-py
- TPOT Demos:https://github...
