Further reading
The following are a few resources that you can explore for a detailed study:
- Learning From Data by Yaser Abu-Mostafa: https://work.caltech.edu/lectures.html
- Curse of Dimensionality—Georgia Tech: https://www.youtube.com/watch?v=OyPcbeiwps8
- Dummy Variable Trap: https://www.learndatasci.com/glossary/dummy-variable-trap/
- Using deep learning to learn categorical embeddings: https://pytorch-tabular.readthedocs.io/en/latest/tutorials/03-Neural%20Embedding%20in%20Scikit-Learn%20Workflows/
- Handling categorical features—CatBoost: https://catboost.ai/en/docs/concepts/algorithm-main-stages_cat-to-numberic
- Exploring Bayesian Optimization—from Distil.pub: https://distill.pub/2020/bayesian-optimization/
- Frazier, P.I. (2018). A Tutorial on Bayesian Optimization. arXiv:1807.02811 [stat.ML]: https://arxiv.org/abs/1807.02811
- Time series clustering using
tslearn: https://tslearn.readthedocs.io/en/stable/user_guide...
