Adapted Jupyter Notebooks to go along with Computerphile's "Data Analysis with Dr Mike Pound" playlist on Youtube.
This repository is meant for educational purposes. I do not claim authorship of the original code.
All code sourced from R scripts shown in the Youtube series, aside from practical adaptations and topical complements. Credits go to Dr. Mercedes Torres-Torres and Dr. Michael Pound of the University of Nottingham's Computer Science Department.
- Episode 3: Chocolate Bar Ratings
- Episode 4: Becker, Barry and Kohavi, Ronny. (1996). Adult. UCI Machine Learning Repository. https://doi.org/10.24432/C5XW20.
- Episodes 5 and 6: Defferrard, Michaël, Benzi, Kirell, Vandergheynst, Pierre, and Bresson, Xavier. "FMA: A Dataset for Music Analysis." In 18th International Society for Music Information Retrieval Conference (ISMIR), 2017. Available at arXiv:1612.01840.
- Episode 8: Quinlan, J. R.. Credit Approval. UCI Machine Learning Repository. https://doi.org/10.24432/C5FS30.
- Episode 9: Hamidieh, Kam. (2018). Superconductivity Data. UCI Machine Learning Repository. https://doi.org/10.24432/C53P47.
- Episode 6: Figure "Two equivalent views of principal component analysis" by Alex Williams. Part of the blog post "Everything you did and didn't know about PCA" published March 27, 2016. You can read the full blog post here.
- Episode 9: Figures courtesy of Oscar García-Olalla Olivera. Part of the blog post "Artificial Neural Networks: What they are and how they’re trained – Part I," published September 16, 2019, in the Xeridia blog. You can read the full blog post here.