This is a very different post than the usual for this blog, but for this very reason it is important. I write about one of the paper’s that has had the biggest influence on me: Audre Lorde’s “The Master’s Tools Will Never Dismantle the Master’s House”.

Video and lecture notes from a tutorial on Probability and Statistics given at PyData NYC 2019. This tutorial provides a crash course in probability in statistics that will cover the essentials including probability theory, parameter estimation, hypothesis testing and using the generalized linear model all in just 90 minutes!

When you train a logistic model it learns the prior probability of the target class from the ratio of positive to negative examples in the training data. If the real world prior is not the same as your training data, this can lead to unexpected predictions from your model. Read this post to learn how to correct this even after the model has been trained!

In this post we’ll explore how we can derive logistic regression from Bayes’ Theorem. Starting with Bayes’ Theorem we’ll work our way to computing the log odds of our problem and the arrive at the inverse logit function. After reading this post you’ll have a much stronger intuition for how logistic regression works!

In this post we're going to take a deeper look at Mean Squared Error. Despite the relatively simple nature of this metric, it contains a surprising amount of insight into modeling.

Kullback–Leibler divergence is a very useful way to measure the difference between two probability distributions. In this post we'll go over a simple example to help you better grasp this interesting tool from information theory.

A guide for getting started with Bayesian Statistics! Start your way with Bayes' Theorem and end up building your own Bayesian Hypothesis test!