First Experiences with Scikit-Learn

I recently made a submission to one of Kaggle’s introductory machine learning competitions. The Python code that I wrote was built upon code I wrote for the Coursera Machine Learning by Stanford class in GNU Octave. For the course we put together implementations of common machine learning models, one of those being the logistic regression model I wanted to use for the aforementioned Kaggle competition. I hadn’t written code in Python in a while and felt that porting those models from GNU Octave to Python/Pandas/NumPy would be a great way of getting familiar with the language again. Continue reading “First Experiences with Scikit-Learn”

Completed Machine Learning by Stanford University Course on Coursera

This post is a bit behind schedule but better late than never as they always say. On October 15th I completed the Machine Learning by Stanford University course on Coursera. I had been interested in making a start in machine learning and hoped that the course would provide a good foundation for building upon.

Continue reading “Completed Machine Learning by Stanford University Course on Coursera”

Salt & Pepper Noise and Median Filters, Part II – The Code

In my first post on salt & pepper noise (hereon s&p noise) and median filters I gave an overview what s&p noise is, why it occurs, and how we can tackle getting rid of it. As discussed, median filters are especially effective at removing s&p noise from images. At the end of the last post I promised to delve into the code behind generating an image with s&p noise and the filters to remove it. Read on for code extracts and explanations. Continue reading “Salt & Pepper Noise and Median Filters, Part II – The Code”