These are the coding exercises from Andrew Ng's Machine Learning course. I've gone through the whole course and finished all of them. All of the source codes are written in Octave, which is really like MATLAB but open source and free. Throughout the course, I have to implement machine learning algorithms from scratch, such as linear regression, logistic regression, neural network and recommendation.
If you want to check it, please either downlaod Octave or MATLAB. Once you have IDE installed, import one of the exercises to Octave or MATLAB, then you should be good to go.
| Exercise | Description |
|---|---|
| ex1-linear-regression | Implement linear regression and get to see it work on data. |
| ex2-logistic-regression | Implement logistic regression and apply it to two different datasets. |
| ex3-multi-classification | Implement one-vs-all logistic regression and neural networks to recognize hand-written digits. |
| ex4-neural-network | Implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition. |
| ex5-regularization-bias-variance | Implement regularized linear regression and use it to study models with different bias-variance properties. |
| ex6-support-vector-machine | Implement support vector machines (SVMs) to build a spam classifier. |
| ex7-kmeans-and-pca | Implement the K-means clustering algorithm and apply it to compress an image. Next, do principal component analysis to find a low-dimensional representation of face images. |
| ex8-anamoly-detection-and-recommendation-system | Implement the anomaly detection algorithm and apply it to detect failing servers on a network. Next use collaborative filtering to build a recommender system for movies. |
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