Click the Launch Binder tab below tolaunch a live instance of the Jupyter notebooks in this repo.
This repo contains iteractive demos of several machine learning algorithms introduced in the introductory lectures for Udacity's Machine Learning Nanodegree program. These interactive demos come in the form of Python based Jupyter notebooks (these are files with the extension .ipynb), which is a convenient open source platform for using Python to perform machine learning (and more generally computational science) tasks.
A short description of the notebooks in this repo:
nonlinear_demo.ipynb - this allows you to play around with several popular algorithms including: kernelized Support Vector Machines, neural networks, and decision trees. Using two dimensional toy datasets you can compare how an individual algorithm performs over a range of its parmeter values, as well as compare how algorithms stack up against each other.
To see a rubric for this project (a high level overview of what we hope you gain from using these notebooks) see MLND_lab_classification_rubric.pdf, located in this repo.