Exercises of the Machine Learning course at Stanford where each one has the following topics:
Ex 1:
* Implementations of linear regression and get to see it work on data:
- Profit prediction for a food track using linear Regression
- Price of Houses prediction with Linear regression using multiple variables
Ex 2:
* Implementation of logistic regression and apply it to two different datasets:
- Prediction where students gets admitted into a University using logistic regression
- Prediction whether microchips from a fabrication plant passes quality assurance (QA) using logistic regression
Ex 3:
* Recognize hand-written digits using logistic regression and neural networks
* Implementation of one-vs-all classification by training multiple regularized logistic regression clasifiers
Ex 4:
* Implementation of backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition
Ex 5:
* Implementation of regularized linear regression and use it to study models with different bias'variance properties.
- Prediction of the amount of water flowing out of a dam using the change of water level in a reservoir with regularized linear regression
Ex 6:
* Implementation of Support Vector Machine (SVM) to build a spam filter
Ex 7:
* Implementation of K-means clustering algorithm and apply it to compress an image.
* Use of principal component analysis (PCA) to perform dimensionality reduction of face images.
Ex 8:
* Implementation of anonimaly detection algorithm and apply it to detect anomalous behavior in server computers.
* Implementation of colaborative filtering to build a recommender system for movies.