This repo includes skeletons, data and solutions to the Coursera Machine Learning Course by Andrew Ng.

The course was taught in Octave and this repo uses Python instead. This is to help others to visualize their machine learning software solutions in a general purpose language. It also serves as an example for the common libraries used in scientific computing and machine learning. Examples are shown how machine learning algorithms can be used with the scikit-learn library.

• numpy
• scipy
• matplotlib
• scikit-learn
• nltk

The repo is divided into 3 main folders, including skeletons, data and solutions.

• Skeletons
  • This stores the skeleton files, which you can fill in the missing parts. You are encouraged to go through the template files before reading the solutions.
• Data
  • This stores all the data used in various exercises. This means you can simply replace a data file with another set of data and see the results using the exercise programs.
• Solutions
  • All files are identical to template files with all the missing parts filled in.

All Solutions licensed under MIT License. See LICENSE.txt for further details.