artificial intelligence

Instance Hardness analysis in Machine Learning

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

Research, code, algorithms and all related to artificial intelligence

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

A Python library for Secure and Explainable Machine Learning

Documentation available @ https://secml.gitlab.io

Follow us on Twitter @ https://twitter.com/secml_py

A library of blog posts and tutorials. Part of the End-to-End Machine Learning School at e2eml.school/courses

Python Panamá Meetup Vol 41 Usando Servicios Administrados de AI de AWS con Python y Boto3 https://www.meetup.com/python-panama/events/300787609/

This AI-powered application assists users in mastering new content by providing personalized guidance based on their learning objectives. Users input their areas of focus, and the application offers tailored support to help them achieve mastery.

AgriTracka is a comprehensive agricultural monitoring platform that leverages sensor data and machine learning algorithms to predict plant health and provide actionable recommendations to farmers, ultimately enhancing crop yield and sustainability. By integrating hardware sensors, data analytics, and AI-driven insights, AgriTracka empowers farmers to make informed decisions and optimize agricultural practices for improved crop management and productivity.

Neural Network software for identifying atoms and atomic columns in High Resolution Transmission Electron Micrographs (HRTEM)

Materials in this project correspond to the "Machine Learning for Bioinformatics" course Dr. Jacob Machado gave at UNC Charlotte during the Fall semester of 2022. See more details in the README.md file.

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a EMOTION RECOGNITION PROXIA (B) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a VOICE PROXIA (B) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a HEARING PROXIA (B) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a TRAFFIC COUNTING (TC) PROXIA when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an OBJECT MASKING PROXIA when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an OBJECT DETECTION proxia when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing an OBJECT DETECTION proxia when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY).