There are 1 repository under google-collab topic.
Accident Detection Model using Deep Learning, OpenCV, Machine Learning, Artificial Intelligence.
FFmpeg 6.0 for Google Colab
The project objective is to generate automated commentary for the cricket videos using computer vision and neural networks
Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.
Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, in this application, we are asking a YouTuber to enter the channel id and a particular timeline. By using the channel id and timeline we are performing sentiment analysis on his videos by fetching the subtitles of their videos in a particular timeline given by the YouTuber.Basically performing intent and emotion classification on his video subtitles.
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. Simulated annealing is a probabilistic technique for approximating the global optimum of a given function.
YBI Foundation Internship : Hands-on Project and Capstone Project
Performed ETL processes in the cloud to upload a dataframe to an RDS instance and used PySpark to perform a statistical analysis on Amazon datasets.
Michigan State University Data Analytics Neural Network Challenge
A groundbreaking initiative aimed at enhancing the independence and quality of life for individuals suffering from blindness and visual impairment. Navigating the world with limited vision presents numerous challenges, and our project addresses these difficulties through the integration of artificial intelligence and computer vision technologies.
Machine Learning Models
The aim of this project is to develop a robust sentiment analysis system that can automatically classify restaurant reviews as positive, negative, or neutral based on the sentiment expressed in the text.
Data analysis project using PySpark to perform the ETL process to extract data, transform it, connect it to AWS RDS, and load it into pgAdmin. Lastly, performed an analysis via PySpark, Pandas, & SQL to determine biases on reviews.
Sets of dashboards created from example data
Terceira edição da Imersão Dados da Alura (03 a 07/05/21). O projeto dessa edição foi inspirado em um desafio do Laboratory Innovation Science at Harvard disponibilizado no Kaggle.
Python, Google collab, data mining done during 6th semester
:bar_chart: Data Science projects: learning and challenges
This is my graduate thesis, a mobile applicaiton with computer vision
Potato disease classification model
Repositorio con análisis de los data ENAHO de los módulos...
Web Application that suggests activities based on user's speech emotion recognition using ML pre-trained model
Final Project of Applied AI about text classification on unlabeled data using NLI and BERT
This project showcased the ETL process of big data. Raw data about Amazon video games reviews was collected from a site, placed into an AWS database, and queried against using Pyspark and SQL to find out whether Amazon vine reviews influenced customer feedback.
A Website For Farmers To Guide Them Regarding Crop Prouction In Their Native Language
AI Mask Detector using Deep learning convolutional neural network
About Maze solving using dijkistra algorithm
This is the final Project of kelas Dasar Machine Learning on Dicoding
Proyecto de Inmersión de Datos en Python, organizado por la empresa Alura Latam donde aprendimos un poco del análisis de datos con python trabajando con una base de datos de un banco alemán para definir que modelo de machine learning sería el indicado para saber si un cliente pagará o no un crédito
models built using various machine learning techniques to predict floods and earthquakes
This project was done as a part of Data Analytics (DA) subject's Capstone Project
Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.