This repository will cover my programming assignments for Coursera's online course on Machine Learning by Dr. Andrew Ng at Stanford University.

https://www.coursera.org/learn/machine-learning

It's an 11-week course covering an introduction to machine learning, including: logistic regression, neural networks, support vector machines, dimensionality reduction, and unsupervised learning.

Applying logistic regression with first- and higher-order decision boundaries, regularization, and multiple classes.

• lr.py - Basic 2-class linear regression
• rlr.py - Regularized linear regression
• mclr.py - Multi-class linear regression

Applying multi-layer neural networks to more efficiently and accurately predict classifications.

• wnn.py - Pre-weighted, 3-layer neural network

All of the data used in the machine learning software is kept here as CSV files.

• ex2data1.csv - 100 samples of two exam scores and a admission or rejection decision (used by lr.py)
• ex2data2.csv - 118 samples of two tests and an overall pass or fail (used by rlr.py)
• ex3data1.csv - 5000 20x20 pixel images of hand-written numbers between 0 and 9 (used by mclr.py and wnn.py)