Data scientist at Port Jackson Partners in Sydney, Australia. My PhD was in computational biology. In my spare time I write about medical research at BioSky.co.CVAbout
I found an excellent tutorial series on Machine Learning on the Google Developers YouTube channel this weekend. It uses Python, scikit-learn and tensorflow and covers decision trees and k-nearest neighbours (KNN).
I really liked the focus on understanding what was going on underneath the hood. I followed along and implemented KNN from scratch and expanded on the base class they described to include the ability to include k as a variable. You can find my implementation in a Jupyter Notebook here.
Sidenote: If you want to visualise the decision tree, you’ll need to install the following libraries. I used homebrew to install graphviz but you could also use a package manger on Linux:
brew install graphviz pip3 install pydotplus