I have completed the ML course taught by Andrew Ng on Coursera and implemented the assignments in Python. So everything have been written from scratch. When I got stuck while understanding the assignment in the concept of python ,because assignments actually are meant for matlab, I got help from @kaleko who is a data scientist and python expert, so some parts were taken or imitated from him.
For better understanding of the codes in the case of lack of comment, you can read the assignments' pdf.
Some parts are missing because implementing matlab assignment in python is not easy. So I did my best.
While running the codes directly, you should know that some parts are commented because of the flow of the assignment.
Data visualization and linear regression from scratch. First part is without vectorization, second part is with vectorization.
Data visualization and logistic regression from scratch. First part is without regularization, second part is with regularization.
Logistic regression with neural network to recognize hand-written digits. Neural network part is missing because the optimizing funtion that the instructor was using for this part was in matlab library, so I couldn't be able to figure it out for python.
Backpropagation for neural network algorithm.
Regularized linear regression and used it to study different bias-variance models.
Spam email classifier with SVM with scikit-learn.
K-means clustering implemented and used to compress and image. I did not complete the PCA part.
In the first part, anomaly detection algorithm is implemented and applied to detect failing servers on network. Second part, collaborative system used for movie recommender system.