jaime.lucero

Document embeddings are one of the simplest ways to give machines an understanding of text, and in our previous article, Document Embeddings Explained: A Guide for Beginners, we explored how they turn entire documents into dense numerical vectors that capture meaning and context. Now that you understand what embeddings are and why they’re useful for tasks like semantic search, classification, and clustering, this tutorial will show you how to generate them in practice using Python. Whether you’re working with short paragraphs, long articles, or a collection of documents, the steps in this guide will help you create embeddings that you [...]

Every day, billions of lines of text, emails, articles, and messages are created online. Making sense of all this unstructured data is one of the toughest challenges in modern AI. Document embedding is a fundamental concept that overcomes this problem. These are dense, numerical vectors that transform words, sentences, or entire documents into meaningful points in a high-dimensional space. These vectors capture the meaning and context of the original text. Because of this, machine learning models can measure similarity and perform tasks like topic classification, semantic search, and recommendation. What are Document Embeddings? Document embeddings convert text into numerical representations, [...]

Before machine learning (ML) models can generate predictions or insights, the raw data must first be cleaned, organized, and transformed into a suitable format for the model. This process is known as data preprocessing. It is the foundation of every successful ML project. It ensures that the model learns from high-quality, consistent, and well-structured input rather than noisy, incomplete, or biased information. In this hands-on guide, we’ll walk through how to transform a raw Kindle eBook dataset from Kaggle into machine learning-ready data using Google Colab, a free cloud-based environment that allows you to write and execute Python code directly [...]