Skip to content
Machine Learning Coursera course offered by Andrew Ng from Stanford University
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
README.md
computeCentroids.m
computeCost.m
computeCostMulti.m
computeNumericalGradient.m
costFunction.m
costFunctionReg.m
displayData.m
drawLine.m
ex7.m
ex7_pca.m
featureNormalize.m
findClosestCentroids.m
fmincg.m
gradientDescent.m
gradientDescentMulti.m
kMeansInitCentroids.m
learningCurve.m
linearRegCostFunction.m
lrCostFunction.m
oneVsAll.m
pca.m
plotDataPoints.m
plotFit.m
plotProgresskMeans.m
polyFeatures.m
predict.m
predictOneVsAll.m
projectData.m
randInitializeWeights.m
recoverData.m
runkMeans.m
sigmoid.m
sigmoidGradient.m
trainLinearReg.m
validationCurve.m

README.md

Machine-Learning from Coursera as offered by Andrew Ng from Stanford

Statement of Accomplishment: https://github.com/fissehab/fissehab.github.io/blob/master/figures/mlandrew.pdf (Grade Achieved: 100.0%)

• Introduction

• Linear Regression with One Variable

• Linear Algebra Review

• Review Questions (for the week's topics)

• Linear Regression with Multiple Variables

• Octave Tutorial

• Review Questions (for the week's topics)

• Programming Exercise 1 (Linear regression)

• Logistic Regression

• Regularization

• Review Questions (for the week's topics)

• Programming Exercise 2 (Logistic regression)

• Neural Networks: Representation

• Review Questions (for the week's topics)

• Programming Exercise 3 (Multi-class classification and neural networks)

• Neural Networks: Learning

• Review Questions (for the week's topics)

• Programming Exercise (Neural network learning)

• Advice for Applying Machine Learning

• Machine Learning System Design

• Review Questions (for the week's topics)

• Programming Exercise (Bias-variance)

• Support Vector Machines (SVMs)

• Review Questions (for the week's topics)

• Programming Exercise (SVMs)

• Clustering

• Dimensionality Reduction

• Review Questions (for the week's topics)

• Programming Exercise (K-Means and PCA)

• Anomaly Detection

• Recommender Systems

• Review Questions (for the week's topics)

• Programming Exercise (Anomaly Detection and Recommender Systems)

• Large-Scale Machine Learning

• Example of an application of machine learning

• Review Questions (for the week's topics)

You can’t perform that action at this time.