The XGBoost library

The XGBoost library for Machine Learning

XGBoost is an open-source library that has gained considerable popularity in the data science community for its effectiveness in solving a wide range of supervised machine learning problems. This library, primarily developed by Tianqi Chen, offers a powerful tree boosting algorithm that relies on successive iterations to improve model accuracy. One of its standout features is the ability to easily handle missing data during the training process, significantly simplifying the workflow for users.

Machine Learning - The scikit-learn library

Scikit-learn, a versatile and powerful tool for Machine Learning in Python

In the modern data era, machine learning has become an essential component for extracting meaningful insights and data-driven decision making. In this article, we will explore the features and capabilities of the Scikit-learn library, a versatile and powerful tool for machine learning in Python. From data preparation to model building and performance evaluation, Scikit-learn offers a wide range of tools to tackle a variety of machine learning problems.

Introduction to the OpenCV library

Introduction to OpenCV, the library for image processing and computer vision

OpenCV, an acronym for Open Source Computer Vision Library, is an open source programming library developed primarily for image processing and computer vision. It was originally developed by Intel in 1999 and has since grown to become one of the most popular and powerful tools for computer vision, with widespread adoption in fields ranging from academic research to industry.

OpenCV - The watershed algoritm

Opencv – Watershed’s algorithm for image segmentation

The Watershed algorithm is an image segmentation technique that aims to separate an image into regions or segments based on gradient information. This algorithm is especially useful in scenarios where you want to separate close or touching objects in an image. Watershed’s approach simulates the image as a topographical landscape, where peaks represent local maxima and valleys represent separations between objects. The algorithm then fills these “tanks” with water, starting from local minima and merging the pools when water from different pools meets.

Sampling methods with Python

Sampling Methods in Python

Sampling is a fundamental process in research and statistics, allowing meaningful conclusions to be drawn from a representative subset of a larger population. In this article, we will review the concept of sampling and the main methods used to select representative samples. Through practical examples in Python code and theoretical considerations, we will illustrate the importance of careful sample selection and the applications of different sampling methods.