Classificação de Imagens

Exploratory Data Analysis of Conference on Neural Information Processing Systems (1987-2017)

Big Data using PySpark, AWS, pgAdmin, postgreSQL, and Google Colab Notebooks to analyze if there any bias towards paid Amazon Reviews.

Google Colab Turtle Graphics! 🐢

Beginner level Sentiment Analysis of tweets

Machine Learning using KNN Classifier and Visualization on Handwritten USPS Dataset

Explore the intricacies of Arnold's Cat Map 🐱🗺️ in this repository. Dive into its chaotic mathematical transformation through code execution and visual representation. Designed for scholarly inquiry, the project facilitates empirical experiments with guided code and interactive examples.

Trabalhos da disciplina de Inteligência Artificial Aplicada a Saúde

Machine Learning Tokyo Tensorflow JS Workshop project

Perform ETL on a dataset from Amazon using PySpark.

The input dataset will consist of images containing Hindi characters. The challenge is to identify the presence of a character in images using Convolutional Neural Networks.

Sentiment Analysis of Yelp Review Data

Exploring Google BigQuery's ethereum dataset to query balances of richest addresses on ethereum blockchain

Кваліфікаційна робота бакалавра. Тема: «Прогнозування динаміки Індексу українських акцій (UX) за допомогою методів технічного аналізу та машинного навчання»

Data-Exploration-Pandas-College-Major

The "Diabetes Prediction" project focuses on predicting the likelihood of an individual having diabetes using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), and Support Vector Machines (SVM), this project offers a comprehensive solution for accurate classification.

This project focuses on predicting the approval or rejection of loan applications using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), and Seaborn, this project provides an end-to-end solution for loan status prediction.

A guide/template for training the YOLOv8 oriented bounding boxes object detection model on custom datasets.

Creating an AI model which is trained on real data, using Python.