https://www.coursera.org/learn/machine-learning
In this exercise, you will be using support vector machines (SVMs) to build
a spam classifier. 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.
ex6.m - Octave/MATLAB script for the first half of the exercise
ex6data1.mat - Example Dataset 1
ex6data2.mat - Example Dataset 2
ex6data3.mat - Example Dataset 3
svmTrain.m - SVM training function
svmPredict.m - SVM prediction function
plotData.m - Plot 2D data
visualizeBoundaryLinear.m - Plot linear boundary
visualizeBoundary.m - Plot non-linear boundary
linearKernel.m - Linear kernel for SVM
[?] gaussianKernel.m - Gaussian kernel for SVM
[?] dataset3Params.m - Parameters to use for Dataset 3
ex6 spam.m - Octave/MATLAB script for the second half of the exercise
spamTrain.mat - Spam training set
spamTest.mat - Spam test set
emailSample1.txt - Sample email 1
emailSample2.txt - Sample email 2
spamSample1.txt - Sample spam 1
spamSample2.txt - Sample spam 2
vocab.txt - Vocabulary list
getVocabList.m - Load vocabulary list
porterStemmer.m - Stemming function
readFile.m - Reads a file into a character string
submit.m - Submission script that sends your solutions to our servers
[?] processEmail.m - Email preprocessing
[?] emailFeatures.m - Feature extraction from emails
? indicates files you will need to complete