11 IPython Tutorials for Data Science and Machine Learning

The 11 IPythonTutorials
  • Example Machine Learning – Notebook by Randal S. Olson, supported by Jason H. Moore. University of Pennsylvania Institute for Bioinformatics
  • Python Machine Learning Book – 400 pages rich in useful material just about everything you need to know to get started with machine learning … from theory to the actual code that you can directly put into action!
  • Learn Data Science – The initial beta release consists of four major topics: Linear Regression, Logistic Regression, Random Forests, K-Means Clustering
  • Machine Learning – This repo contains a collection of IPython notebooks detailing various machine learning algorithms. In general, the mathematics follows that presented by Dr. Andrew Ng’s Machine Learning course taught at Stanford University (materials available from ITunes U, Stanford Machine Learning), Dr. Tom Mitchell’s course at Carnegie Mellon, and Christopher M. Bishop’s “Pattern Recognition And Machine Learning”.
  • Research Computing Meetup – Linux and Python for data analysis (tutorials). University of Colorado, Computational Science and Engineering.
  • Theano Tutorial – A brief IPython notebook-based tutorial on basic Theano concepts, including a toy multi-layer perceptron example..
  • IPython Theano Tutorials – A collection of tutorials in ipynb format that illustrate how to do various things in Theano.
  • IPython Notebooks – Demonstrations and use cases for many of the most widely used “data science” Python libraries. Implementations of the exercises presented in Andrew Ng’s “Machine Learning” class on Coursera. Implementations of the assignments from Google’s Udacity course on deep learning.