Michael duPont - michael@mdupont.com
This is the workshop resource repository for the Machine Learning workshop at the Orlando Python meetup
This project is designed for Python3.x (though it should still work for Python2.7) and uses the following libraries:
Rather than installing these packages via pip3, it is recommended that they be installed via Anaconda. It is a pre-compiled collection of scientific Python libraries.
To run the notebooks, open a terminal in the repository's directory and run jupyter notebook. This will open a Jupyter file browser in your default web browser.
The five Jupyter notebooks are in the root directory:
- Intro to Scikit-Learn: Introduction to concepts and terminology. Run through two basic supervised learning alorithms
- Classifier Comparison: Compares a variety of supervised classification models. Recreates the graphic on sklearn's home page
- Affinity Propogation: Example of an unsupervised clustering algorithm on randomized, generated blob data
- Digits Classification: Supervised classifier for handwritten digit recognition. Dataset sourced from MNIST
- Eigen Faces: Combines an unsupervised dimensionality reduction and a supervised classifier for facial recognition. Dataset sourced from the University of Massachetts
Press Shift-Enter to run a selected block.
Any pre-existing source code can be found in sklearn-source.
Final HTML versions of the notebooks can be found in final-reports.