The notebooks in this repository contain explanations and implementations of a number of basic machine learning algorithms. The contents of the notebooks are largely derived from stuff I've read and learned from a handful of other sources. These sources include the Coursera classes on machine learning and neural networks, the book "Machine Learning: A Probabilistic Approach", and some of the tutorials on the scikit-learn website. So there's not much new here, and I've simply written up a bunch of material involving examples from these sources for the purposes of self-education. Feel free to make use of the notebooks for similar purposes. Corrections to any errors I've made are also welcome.