Machine Learning Recipes

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

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Computational biology PhD candidate at the Australian National University. I love writing (both articles and software), learning more about the world around us, and beekeeping. I also write for BioSky.co

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