An analytical and example-based comparison between three well-known text representation approaches, in the context of downstream machine learning modeling with scikit-learn.
Author Archive | Iván Palomares Carrascosa
Document Clustering with LLM Embeddings in Scikit-learn
This insightful, hands-on article guides you on using LLM embeddings of a collection of documents for clustering them based on similarity, and potentially identifying common topics among documents in the same cluster.
A Beginner’s Reading List for Large Language Models for 2026
A list of relevant readings to put under your radar if you are beginning in the world of LLMs in 2026.
The Beginner’s Guide to Computer Vision with Python
This article is designed to guide beginners interested in computer vision into the implementation of three fundamental computer vision tasks: image processing, object detection, and image classification.
10 Ways to Use Embeddings for Tabular ML Tasks
10 insightful strategies to use embeddings for leveraging data at its fullest in a variety of ML tasks, models, or projects as a whole.
5 Python Libraries for Advanced Time Series Forecasting
In this article, you will learn five Python libraries that excel at advanced time series forecasting, especially for multivariate, non-stationary, and real-world datasets.
The Machine Learning Engineer’s Checklist: Best Practices for Reliable Models
In this article, you will learn a practical, research-informed checklist of best practices that help machine learning engineers build systems that remain reliable long after deployment.
Transformer vs LSTM for Time Series: Which Works Better?
Training and comparing two robust deep learning architecture for a single, common time series analysis task: all step-by-step.
3 Subtle Ways Data Leakage Can Ruin Your Models (and How to Prevent It)
In this article, you will learn what data leakage is, how it silently inflates model performance, and practical patterns for preventing it across common workflows.
Prompt Engineering for Time Series Analysis
Seven prompt engineering strategies that can be used to leverage time series analysis tasks with LLMs.