Machine-Learning-Exercise

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Machine-Learning-Exercise

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There are my codes of the exercises in Andrew Ng's Machine Learning course on Coursera

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# Machine-Learning-Exercise
There are my codes of the exercises in Andrew Ng's Machine Learning course on Coursera.
This project is to learn how to use Github and record my learning of Machine Learning.

### Ex1
Linear regression of Single and Multiple feature using Gradient Decent
##### 1st submission
Score: 100%

### Ex2
Logistic regression and Regularization using advanced optimization algorithm
##### 1st submission
Score: 100%

### Ex3
Logistic regression and Neural Networks for multi-classification (parameters of NN are provided)
##### 1st submission
Score: 80%  
Error: Logistic regression is not suitable for dataset of any size
##### 2nd submission
Score: 100%

### Ex4
Feedforward and Backpropgation of Neural Networks (with and without regularization)
##### 1st submission
Score: 75%  
Error: When calculating regularization term of cost function, the parameters of biases are not ignored
##### 2nd submission
Score: 100%

### Ex5
Use train set, cross-validation set and test set to choose lambda of Regularization, the features used in Polynomial regression and the size of train set
##### 1st submission
Score: 80%  
Error: when using the sub-set of the train set, the size is not changed to the size of the sub-set
##### 2nd submission
Score: 100%

### Ex6
Try linear kernel and Gaussian kernel of Support Vector Machine; try different C and sigma parameters of Gaussian kernel  
Implement a simple spam classifier by using SVM
##### 1st submission
Score: 100%

### Ex7
Achieve K-means and Principle Component Analysis; apply K-means to image compression; apply PCA to face image compression
##### 1st submission
Score: 100%

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