Statistics

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 Developers – great resource for those wanting to learn and/or deepen their understanding of Python.
• Machine Learning with scikit-learn – provides a good introduction and background to machine learning.
• Machine learning with Python – covers regression, neural networks, decision trees.
• Machine Learning with Python – covers PCA, k-means clustering, k-nearest neighbours.
• Learn Data Science with Python – covers regression, random forests, k-means clustering.
• Probabilistic Programming & Bayesian Methods for Hackers – covers Bayesian methods including Markov Chain Monte Carlo.
• Bayesian data analysis – covers how probabilistic programming works.
• Supervised Learning SVM – 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.