Probabilistic models are great at promoting good science. I.e. we're trying to model features to predict outputs. In this Python Notebook you will learn how to calculate bayes rule and use a naive bayes classifier.
A quick Python Notebook to show you how to use statsmodels to detrend seasonal data.
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.
Learn how to monitor microservices with Prometheus. Expert-led introduction from Phil Winder.
Dr. Phil Winder
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7 minMonitoring != Logging != Tracing. Learn the differences between these three fundamental cloud-native concepts. Find out the uses cases for each technology.
Dr. Phil Winder
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2 min
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.
Case studies and industry analysis from our team. No hype, roughly monthly.