Este repositorio esta basado principalmente en la carrera de machine learning y data science de platzi pero también habrán recursos de otras plataformas e instituciones educativas.

Python library to open/close applications in windows.

Develop software using AI Agent teams (CrewAI framework)

Introducing ChameleonAI, your very own, customizable roleplaying AI chatbot. Powered by Google's state-of-the-art PALM Generative AI Model, you get to talk to anything, anyone, anywhere!

🧠️🖥️2️⃣️0️⃣️0️⃣️1️⃣️🔠️🔢️ The linguistic:Chinese-Simplified category for AI2001, containing Chinese (Simplified) language linguistic datasets

𝗔𝗿𝘁𝗶𝗳𝘆 𝗔𝗜 is a powerful 𝘁𝗲𝘅𝘁-𝘁𝗼-𝗶𝗺𝗮𝗴𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 🖼️ that transforms your text descriptions into stunning images 🎨 using advanced 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 🤖. Built with Flutter, 𝗔𝗿𝘁𝗶𝗳𝘆 𝗔𝗜 offers a seamless and interactive user experience 🚀 for creating unique visuals from text prompts ✍️.

Data Science With Python->Target: AI/ML

2D Max Pooling from NumPy

Here are all my code files of Advanced AI/ML architectures built from scratch using Pytorch.

🧬 Trabalho da cadeira de Inteligência Artificial (CC0027).

RepDetect is an android mobile application for workout enthusiast which uses Google MediaPipe Pose landmark detection using MLKit to create a basic fitness application.

Minhas anotações que fiz durante o curso de machine learning da FreeCodeCamp

Project for Artificial Inteligent classes created with @SmoothCrimminal @Maciasty15 and @Kajgal.

The Big Bang of Data Science- Code AI from the Start to The End- [Book Four]

This core project integrated IDX, programming environment with AIs and Google's Android, desktop and iOS emulators. With early access, I explored the synergies between these technologies, marking a key breakthrough in my experience and understanding. 🌐🤖 #InnovativeProject

Artificial Intelligence course

Data science Project (Fruits_Recognition)

Our solution to the final challenge of "Artificial Intelligence" course @ Columbia University, Fall 2021

Source code for identification of diagnostic blood biomarkers IBD patients and by applying machine learning on transcriptomics data

The project aims to create a Supervised Machine Learning algorithm for cat classification problem that detects whether a cat is present in the image or not by generating an RGB/HSV Histogram Model. By processing a large set of images, the specific features are being identified with an accuracy of up to 83%.