## How to build your own ChatGPT

Photo by Miguel Á. Padriñán Introduction Let’s talk about the hype In recent months, ChatGPT and its many capabilities have take the world by storm. If you have made use of NLP through your career, the leaps and bounds improvement represented by ChatGPT are pretty mind-boggling. We’ll take the next few minutes to demystify what …

## R to Python: A Guide to Recreating Dplyr’s Convenient Joins in Python

Photo by Alexander Grey introduction If you are one of the many R users who is making the shift to python, you may find yourself depending on the convenience of some of R’s most beloved libraries. At the surface, the jump from the convenience and simplicity of R can seem a bit daunting as the …

## Understand the Internet’s Relevance Metric in a Hurry: TF-IDF

intro With roots in the 1950’s, TF-IDF is a cornerstone for modern applications determining the relevance of each word in a document. At first glance, one could take the simple approach of looking at volume alone, i.e. “how many times did each term show up?”; but TF-IDF takes us a big step further; not only …

## Simulating Probability in R: The Classic Monty Hall Problem

Image by Arek Socha from Pixabay  Introduction Over the course of this post, we’re going to learn about using simulation to understand probability and we’ll use the classic example of the Monty Hall gameshow problem. Monty Hall had a gameshow back in the day, where he showcased the following problem. If you’re not familiar with him or they …

## Statistical Significance Versus Random Chance: The Intuitive Explanation of P-value

Image by Greg Montani from Pixabay  Introduction Welcome to this lesson on calculating p-values. Before we jump into how to calculate a p-value, it’s important to think about what the p-value is really for. Hypothesis Testing Refresher Without going into too much detail for this post, when establishing a hypothesis test, you will determine a null hypothesis. Your …

## Linear Regression Simulation to Understand Slope Sensitivity

Introduction Over the next few minutes, I’ll send you on your way to leveraging linear regression for a bit more than explanation or prediction, rather you’ll utilize them to for the sake of inference. We will leverage simulation for inference in three ways: Understanding model sensitivityp-valueconfidence intervals In this post, we’ll mostly be exploring the …

## Understand Customer Churn With The Chi-squared Test Statistic

Introduction The chi-square statistic is a useful tool for understanding the relationship between two categorical variables. For the sake of example, let’s say you work for a tech company that has rolled out a new product and you want to assess the relationship between this product and customer churn. In the age of data, tech …

## How to Visualize Multiple Regression in 3D

Image by Mediamodifier from Pixabay Introduction No matter your exposure to data science & the world of statistics, at the very least, you’ve very likely heard of regression. In this post we’ll be talking about multiple regression, as a precursor, you’ll definitely want some familiarity with simple linear regression. If you aren’t familiar you can start here! Otherwise, …

## Visualizing Multiple Linear Regression with Heatmaps

Image by DavidRockDesign from Pixabay  Introduction No matter your exposure to data science & the world of statistics, it’s likely that at some point, you’ve at the very least heard of regression. As a precursor to this quick lesson on multiple regression, you should have some familiarity with simple linear regression. If you aren’t, you can start here! …

## The Intuitive Explanation of Logistic Regression

Introduction Logistic regression can be pretty difficult to understand! As such I’ve put together a very intuitive explanation of the why, what, and how of logistic regression. We’ll start with some building blocks that should lend well to clearer understanding so hang in there! Through the course of the post, I hope to send you …