The machine learning algorithm that I coded here was proposed in a Coursera course call Machine Learning (https://www.coursera.org/learn/machine-learning).
1 Linear Regression
In this folder, you will find 9 file. Works for both, simple and multiple linear regression. I list them below and explain what each one do:
- costFunction: creat a Cost Function to Linear Regression
- featureNormalize: normalizes the feature
- gradientDescent: computes the Gradiente Descent
- normalEquation: computes the Normal Equation
- plotData: to plot the data with 2 variables
- SRL: a script for Simple Linear Regression (just a example)
- MRL: a script for Multiple Linear Regression (just a example)
- data1: data set 1
- data2: data set 2
2 Logistic Regression
In this folder, you will find 11 file. I list them below and explain what each one do:
- costFunction: creat a Cost Function to Logistic Regression
- fmincg: to optimize the cost function (I didn't code this)
- oneVsAll: the one vs all algorithm, used when we have more then 2 class
- plotData: to plot the data
- predict: make the predictions
- predictOneVsAll: make the predictions using the one vs all algorithm
- sigmoid: computes the sigmoid function
- TwoClass: a script for Logistic Regression with 2 class (just a example)
- PlusTwoClass: a script for Logistic Regression with more then 2 class (just a example)
- data1: data set 1
- data2: data set 2
3 Neural Networks
4 Unsupervised Learning