Coursera - Machine Learning by Andrew Ng

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

Exercise 2: Logistic Regression

In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.
To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise.
You can also find instructions for installing Octave/MATLAB in the Environment Setup Instructions" of the course website.

Files included in this exercise

ex2.m - Octave/MATLAB script that steps you through the exercise
ex2 reg.m - Octave/MATLAB script for the later parts of the exercise
ex2data1.txt - Training set for the first half of the exercise
ex2data2.txt - Training set for the second half of the exercise
submit.m - Submission script that sends your solutions to our servers
mapFeature.m - Function to generate polynomial features
plotDecisionBoundary.m - Function to plot classifier's decision boundary
[?] plotData.m - Function to plot 2D classification data
[?] sigmoid.m - Sigmoid Function
[?] costFunction.m - Logistic Regression Cost Function
[?] predict.m - Logistic Regression Prediction Function
[?] costFunctionReg.m - Regularized Logistic Regression Cost
? indicates files you will need to complete