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The repository ml-python contains IPython notebooks outlining example implementations and applications of machine learning algorithms using Python 3, numpy, and maybe pandas.

The motivation for creating these notebooks came from taking Andrew Ng's machine learning course on Coursera. The class implements the algorithms in GNU Octave / MATLAB so I decided to try implementing them using Python as a challenge, to reinforce the concepts, and also just for fun.

These notebooks are intended to serve as examples and references for the basic gist of the outlined machine learning algorithms.

• Linear regression
• Logistic regression
• k-means clustering
• Artificial neural networks (vanilla or using Keras)
• Support vector machines

These are currently in progress at various stages of completion.

• Limitations of k-means clustering
• Cross-validation