The Bootstrap
Recently I’ve had occasion to use the bootstrap and have been reminded at what a remarkably powerful technique this is despite it’s simplicity. I thought…
Look what the Cat dragged in: Catboost with Tidymodels
I’ve been learning the Tidymodels framework for building Machine Learning models in R pioneered by Max Kuhn and Julia Silge. After spending a few weeks…
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Definitely Interesting Matrices
This is a post I've been wanting to write for a while - Quadratic forms and Definite matrices are everywhere in linear algebra and they…
Why You’re Not as Data-Driven as You Think You Are
How many times have you heard someone say they are data-driven or data centric or that “data is the heart of everything they do”? I’ve…
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Shortest Paths: Dijkstra’s Algorithm
Introduction I have a startling admission to make. When I was a student, I scoffed at Dijkstra's algorithm - I had paid it no mind…
Word2vec vs Fasttext – A First Look
Introduction Recently, I've had a chance to play with word embedding models. Word embedding models involve taking a text corpus and generating vector representations for…
ML Classifier Evaluation – A First Look
Once you’ve built a machine learning classifier, the next step is to validate it and see how well it fits the data. This short post…
Naive and Proud: Introducing the Naive Bayes Algorithm
The Naive Bayes Algorithm is a simple and elegant approach for tackling supervised learning problems in Machine Learning. This post will be a brief introduction…
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Gradient Descent: The Workhorse of Machine Learning
If you’re like me, you’ve heard a lot about Gradient Descent. You’ve heard that it is a foundational algorithm for optimising functions which all self…
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Job Searching in Data Science
Job searching can be irritating at best and hopelessly frustrating at worst. This is particularly true in Data Science where the field is still relatively…