A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

Some projects that I've worked on to harness the power of machine learning and data science 🙌🏻

Lua-Based Machine, Deep And Reinforcement Learning Library (For Roblox And Pure Lua). Contains 34 Models!

An overview of all clustering techniques with examples of data.

This repository contains a collection of data science projects which I did during the IBM Data Science Professional certification programme. Each project demonstrates different aspects of data science, data analysis, data visualization and machine learning.

A KMeans implemented in C++ with Python bindings and GPU acceleration

SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks

My personal implementation of several unsupervised learning algorithms.

This project presents a comprehensive evaluation of various unsupervised anomaly detection algorithms applied to datasets with mixed categorical and numerical attributes. The performance will be measured using a suite of metrics that assess clustering quality, anomaly detection precision, and computational efficiency.

Exercises on Machine Learning

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

Unsupervised decoding of hidden markov models from transformer residual streams (for transformers also trained on HMM data and acting as optimal predictors of the HMM)

Reposotorio donde exploro los distintos algoritmos de machine learning en Python y de ser posible tambien en R

The Python library of the Khiops AutoML suite

LoCoMotif is a time series motif discovery method that discovers variable-length motif sets in multivariate time series using time warping

Embark on a transformative "100 Days of Machine Learning" journey. This curated repository guides enthusiasts through a hands-on approach, covering fundamental ML concepts, algorithms, and applications. Each day, engage in theoretical insights, practical coding exercises, and real-world projects. Balance theory with hands-on experience.

Algorithms for outlier, adversarial and drift detection

University Project for Anomaly Detection on Time Series data

we aim to predict trends in the Canadian market basket using sentiment analysis techniques. Sentiment analysis involves analyzing text data to determine the sentiment expressed, whether positive, negative, or neutral.