Foreword: The Efficiency Paradox

The language of the Tunisian people is denied recognition, not only as an official language, but as a language at all. It is dismissed as “darija,” a mere slang. The central thesis of this analysis is that Tunisians should advocate for the official status of their own language. The common misconception that Tunisian is a dialect of Arabic is as historically and linguistically flawed as claiming that French or Spanish are simply dialects of Latin. Indeed, the linguistic distance between Tunisian and Peninsular Arabic exceeds that of recognized separate languages like German and Danish. This essay will deconstruct the baseless justifications for maintaining Arabic’s official status.

Introduction

Introduction

Introduction

Introduction

Introduction

The Small Multiples Revolution

Beyond the Basics

The Matplotlib Problem

In the previous post, we learned the basics of web crawling and developed our first one-page crawler. In this post, we implement something more fun and challenging. Something that every Github user could use: a Github Users Crawler. Disclaimer: This project is intended for Educational Purposes ONLY.

Data is at the core of any AI project. Sooner or later, as ML practitioner, you will run out of data or get tired of using public available API's. So how do we deal with such an obstacle? By implementing our own spider bot, of course!

In the previous post we introduced the confusion matrix in the context of hypothesis testing and we showed how such a simple and intuitive concept can be a powerful tool in illustrating the outcomes of classification models. Now, we are going to discuss various performance metrics that are derived from a confusion matrix.

A 99% accurate model can be worse than useless. The confusion matrix reveals the structure of your model's errors, showing not just how often it fails, but how those failures compound in real-world systems where different mistakes carry vastly different costs.

The least squares method finds the best-fitting line through data by minimizing squared errors - the foundation of linear regression and machine learning. Instead of memorizing formulas, we'll discover the solution by working backwards from what we want to achieve. This approach applies far beyond least squares.

Linear algebra and statistics form the foundation of machine learning. When you understand these topics, you gain insight into how algorithms actually work, and you can even create your own.