## 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…

Skip to content
## Tag: Statistics

##
ML Classifier Evaluation – A First Look

##
Naive and Proud: Introducing the Naive Bayes Algorithm

##
Gradient Descent: The Workhorse of Machine Learning

##
Why “Gradient” Descent?

##
Random Variables and Probability Functions

##
“It’s Not Special” – The Best Advice I Ever Had

##
Probability – Definitions and Axioms

##
Introduction to Probability – Set Theory

##
Nearest Neighbour Classifiers

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…

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…

Continue reading → Naive and Proud: Introducing the Naive Bayes Algorithm

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…

Continue reading → Gradient Descent: The Workhorse of Machine Learning

Recently I was thinking about the gradient descent algorithm and I was bothered was one question - Why do we go in the direction of…

In this post I will build on the previous posts related to probability theory - I have defined the main results of probability from axioms…

Continue reading → Random Variables and Probability Functions

Impostor syndrome is a real feeling and I get it at least twice a day with respect to what I do. Sure, I have a…

Continue reading → “It’s Not Special” – The Best Advice I Ever Had

After the last post discussing set theory here, the next logical step is to discuss the concept of probability theory. The theory of probability is…

Recently I have been undertaking a project to commit my accrued notes relating to statistics online. I want to do this for a few reasons:…

Nearest neighbour algorithms classify unlabelled instances (data observations/ cases) by assigning them to a class which is the most similar found in the training data.…