Source code and content for my personal blog
A lot of the material here is amalgamated from various sources I have read during my studies, sometimes reworded to make it more understandable for myself.
Where content has been reproduced from the Data Science Handbook I am writing on Gitbook you will see a link in the article, along with text similar to "This article has been reproduced from the book I am writing for my own benefit. As the content is updated the Gitbook will receive it first so please use that as your source.".
Observations and thoughts on topics I encounter on the Data Science journey
Credited reprint of articles I've found online as I go, sometimes with summaries and thoughts on the content.
Where possible I have tried to credit the original author, I have no intention of passing off other people's work as my own, but unfortunately I have not captured all the source links since I began. If a passage seems familiar to you, and you know the originating source please contact me so I can update the accreditation.
books: Interesting books I have read, or books that look good and are on my bucket list to read.
learning: Notes on learning techniques or topics to look into that looked interesting.
datascience: Data Science related material, algorithms, feature engineering, performance metrics, and all of the goodness this field covers.