Most of the time you have too many dimensions to simply plot the decision boundary of a classifier. This workshop investigates other ways to visualise under and overfitting in high-dimensional datasets.
Nearest neighbour algorithms are useful for all kinds of problems. This python notebook explains what it is and how to use it.
We can use the nearest neighbour algorithm for classification too. This python notebook explains how to perform the k-Nearest Neighbours algorithm.
What is meant by a complex model? What does overfitting mean? All these questions are answered in this intuitive Python workshop.
A python notebook demonstrating the use of support vector machines.
Logistic regression is very useful for producing probability estimates for classification. This simple workshop shows you how.
We can also use linear models for classification purposes. Find out how in this workshop.
This workshop describes how you can perform regression when you are plagued by outliers.
An introduction to regression with Python and SKLearn
A Python workshop showing you why we need to optimise using gradient descent and provides a simple example.
Case studies and industry analysis from our team. No hype, roughly monthly.