Welcome to my site, Inside Learning Machines! My name is Michael Attard, I’m a data scientist based in Amsterdam, the Netherlands. I have a deep interest in machine learning, and its application to solve real-world problems. My background is in Aerospace Engineering (Bachelors, University of Toronto) and Astrophysics (Masters & PhD, Western University). Please feel free to visit my LinkedIn page.
Throughout my career, I have found an abundance of information online regarding various topics in the data science. However, I have often found the available sources lack a deep insight into how machine learning algorithms actually work. I don’t mean this solely in terms of their mathematical foundations, but also in terms of how they are implemented in code for practical use. All too often, these algorithms are treated as little more than black boxes.
The aim of this site is to dive deep into how machine learning algorithms work: starting with their mathematical derivations, straight through to implementation. The hope is that the reader will gain a deeper understanding of these algorithms; how they are built and in what circumstances they may be applicable to a given problem. It is my belief that a deep understanding is required to properly use machine learning technology.
There are two main types of articles written here:
- Long articles covering a single machine learning algorithm. These posts will outline the motivation, foundational assumptions, mathematical derivation, and implementation of a given algorithm. The implementations are done in Python 3, with the coding examples available on my GitHub. Finally, the implementations are tested on available data to verify their functionality, and solve a particular problem.
- Short articles that focus in on metrics typically used while doing machine learning. The intent of these posts is to describe said metrics, such that the reader will walk away with sufficient knowledge to properly use and understand them. All coding here is also done in Python 3.
If you have any questions or suggestions regarding the articles, please feel free to leave a comment. I sincerely hope you enjoy, and gain value from, Inside Learning Machines.