Bootcamp online analista de dados disponibilizado pelo IGTI – Instituto de Gestão e Tecnologia da Informação

Aula de Inteligência Artificial - IFSP

University teaching files

DeepFake Detection Web-App[Mirage Breaker] 🖥 using Deep Learning(ResNext and LSTM), Flask and ReactJs where you can predict whether a video is FAKE Or REAL along with the confidence ratio.

Learn machine learning with Python through data exploration, visualization, and key libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

VSCODE integration in jupyter-lab

Quantifying spike trains using 3 types of rate codes and also Coefficient of variance of Interspike Intervals

In this repository i am gonna walk you through evolution of face recognition algorithms using deep learning approach.

Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. This package can be imported into any application for adding security features. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99.4% is achieved.

My blog where I talk about Machine learning extensively. Build with fastpages

Top Machine Learning Projects | Best Machine Learning Projects With Source Code

Datasets: Classificação (Aprendizado Supervisionado e não Supervisionado)

Using PySpark, I performed the ETL process on a large dataset (170,000 rows) of Video Games. Next, I created an AWS relational database instance & transformed the data to be loaded into PostgreSQL. Once in PgAdmin, exported the Video Game Review Table as a CSV file. Afterward, I loaded the data into Python to create Dataframes using Pandas. Then…

Converts Garmin GPX files to TXT/CSV.

The Food-Facility-Compliance-Engine leverages advanced ETL and BI technologies to process and visualize inspection data from Sonoma County's official government database, boosting compliance and safety in local food facilities.

Jupyter with GPU acceleration for Windows 10/11

My Python Examples

Determine key metrics about home sales data using SparkSQL and then use Spark to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.

This project applies deep learning to medical image processing, aiming to build an accurate model that identifies COVID-19 infection in radiology images. Using transfer learning with a Residual Network, fine-tuned the model on real chest X-ray images of COVID-19 cases, healthy lungs, viral pneumonia, and bacterial pneumonia.

Predict house prices using XGBoost regression. This project preprocesses data, trains the model, and evaluates predictions to forecast house prices based on various features.