An implementation of various machine learning algorithms (in Python) as a companion to my journey through a Coursera course on Machine Learning. Includes the following topics:

• Linear Regression
• Logistic Regression
• Classification Problems
• Neural Networks
• Evaluation of Learning Algorithms
• Support Vector Machines (SVM)
• K-means Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection
• Collaborative Filtering (CoFi)

Disclaimer: This is my Python implementation of solutions to the programming exercises provided as part of Coursera's free course on Machine Learning. While much of the code for displaying output to the user (especially plotting) has been adapted from the MATLAB/Octave implementation provided in the course materials, all code written is my own (and many of the 'functional' methods I've implemented from scratch, given only the interface). The exercises themselves, the sample datasets used, and the inspiration for all of it are provided courtesy of Andrew Ng, who teaches the Coursera course. This repository is fair use of public material and is meant to be instructive rather than exploitative or plagiarizing.