https://github.com/hassan11196/machinelearningexercises
Machine Learning by Stanford University. Taught by: Andrew Ng
https://github.com/hassan11196/machinelearningexercises
andrew-ng andrew-ng-machine-learning coursera-machine-learning machine-learning matlab ml octave
Last synced: 3 months ago
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Machine Learning by Stanford University. Taught by: Andrew Ng
- Host: GitHub
- URL: https://github.com/hassan11196/machinelearningexercises
- Owner: hassan11196
- Created: 2020-01-18T15:53:25.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-02T05:04:12.000Z (almost 5 years ago)
- Last Synced: 2025-01-05T20:10:37.586Z (5 months ago)
- Topics: andrew-ng, andrew-ng-machine-learning, coursera-machine-learning, machine-learning, matlab, ml, octave
- Language: MATLAB
- Homepage: https://hassan11196.github.io/MachineLearningExercises/
- Size: 57.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Exercises
## Coursera Course : Machine Learning by Stanford University
- [Exercise 1 - Linear Regression](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex1)
- [Linear Regression Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/computeCost.m)
- [Gradient Descent](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/gradientDescent.m)
- [Normal Equations](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/normalEqn.m)
- [Exercise 2 - Logistic Regression](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex2)
- [Logistic Regression Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex2/ex2/costFunction.m)
- [Logistic Regression Regularized Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex2/ex2/costFunctionReg.m)
- [Exercise 3 - Multi-class Classification and Neural Networks](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex3)
- [Multi-class Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/ex3.m)
- [Vectorizing Logistic Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/lrCostFunction.m)
- [One-vs-all Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/oneVsAll.m)
- [Neural Network Prediction Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/ex3_nn.m)
- [Exercise 4 - Neural Networks Learning](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex4)
- [NN Feedforward and Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/nnCostFunction.m)
- [Sigmoid Gradient](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/sigmoidGradient.m)
- [Backpropagation](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/nnCostFunction.m)
- [Exercise 5 - Regularized Linear Regression and Bias vs Variance](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex5)
- [Regularized Linear Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex5/ex5/linearRegCostFunction.m)
- [Polynomial Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex5/ex5/polyFeatures.m)
- [Exercise 6 - Support Vector Machines](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex6)
- [SVM with Gaussian Kernels](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex6/ex6/svmTrain.m)
- [Spam Email Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex6/ex6/processEmail.m)
- [Exercise 7 - K-means Clustering and Principal Component Analysis](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex7)
- [K-means Clustering](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex7/ex7/computeCentroids.m)
- [Dimensionality Reduction with PCA](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex7/ex7/pca.m)
- [Exercise 8 - Anomaly Detection and Recommender Systems](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex8)
- [Anomaly detection](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex8/ex8/selectThreshold.m)
- [Collaborative filltering learning algorithm](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex8/ex8/cofiCostFunc.m)
### Help Material :
[what makes the gaussian kernel so magical for pca](https://stats.stackexchange.com/questions/131138/what-makes-the-gaussian-kernel-so-magical-for-pca-and-also-in-general)
[The Gaussian Kernel](https://mccormickml.com/2013/08/15/the-gaussian-kernel/)
Taught by: Andrew Ng,