There are some amazing Machine learning libraries written in Python (example sklearn). These libraries make it a cinch to fit ML models to training datasets.
From my experience using sklearn to build ML models at Teva UK, I noticed that I barely understood how the learning algorithms I was using worked; these libraries make you lazy. I decided to fill the gaps in my knowledge by getting dirty with the algorithms, learning how they actually work.
This repo will contain my implementations of popular learning algorithms. Please feel free to make contributions.