One of the best things about the iPython notebook is the number of easy-to-follow tutorials it has inspired. I thought I’d share a few that I’ve found on machine learning and statistics.

- Python for DevelopersÂ – great resource for those wanting to learn and/or deepen their understanding of Python.
- Machine Learning with scikit-learnÂ – provides a good introduction and background to machine learning.
- Machine learning with PythonÂ – covers regression, neural networks, decision trees.
- Machine Learning with PythonÂ – covers PCA, k-means clustering, k-nearest neighbours.
- Learn Data Science with PythonÂ – covers regression, random forests, k-means clustering.
- Probabilistic Programming & Bayesian Methods for HackersÂ – covers Bayesian methods includingÂ Markov Chain Monte Carlo.
- Bayesian data analysis – covers how probabilistic programming works.
- Supervised Learning SVMÂ – covers Support Vector Machines (SVM)
- Face Recognition– covers PCA, and SVM.
- Particle Filter – covers the identification and tracking of objects in a video.

I’ll continue to update the list as I find new notebooks I find handy.