A personal corner of the internet
Hey, I’m Eneas. I build software and games, and write about what I learn along the way.
From the notebook
Nginx is often introduced simply as a web server, but in modern web applications it commonly plays a broader role. It can sit in front of an application, receive incoming web traffic, handle HTTPS, serve static files, and forward requests to backend services.
The article introduces the core mathematics needed to begin machine learning without overwhelming the reader with advanced theory. It uses a simple student-score prediction example to explain mean, standard deviation, probability, linear relationships, functions, prediction error, vectors, and matrices. The central idea is that AI math becomes much easier when each concept is tied to a practical purpose. The article shows how data is summarized, how relationships are identified, how models turn inputs into outputs, and how prediction errors can be measured using Mean Absolute Error. It also reinforces that libraries such as NumPy handle the calculations, while the reader’s job is to understand what those calculations mean. The article ends by preparing the reader for the next step: training a real machine-learning model.
This article explains how machine-learning systems represent real-world information as numerical data. It introduces NumPy arrays and shows how they differ from regular Python lists, then builds the foundation for understanding scalars, vectors, matrices, dimensions, and shapes. Through practical examples, it shows how students, images, text, and other real-world data can be converted into numerical structures that AI models can process efficiently.
Once you move from basic Python into data science, machine learning, or AI, one of the first things you need to become comfortable with is working with tables of data. Most AI projects do not begin with a clever model or a complicated algorithm.
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