Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

Gesture recognition library for Python

Joint Deep Neural Networks for Simultaneous Object and Depth Detection

Compressive Strength of Concrete determines the quality of Concrete. analyze the Concrete Compressive Strength dataset and build a Machine Learning model to predict the quality.

I'm a self-taught AI and Machine Learning developer, passionate about AI, Machine Learning, Computer Vision and learning new things. I have good experience working with the Python programming language and its libraries, and I am interested in computer vision and image processing using machine learning and deep learning algorithms.

Here is my Bachelor's Degree Thesis, Music and Feelings: A Deep Learning Approach to Emotional Composition

This project aims to take an chest X-Ray image and detect if the patient has the COVID-19 infection. It uses a CNN to train on a large dataset of both normal and COVID lung images to learn how to process the difference in both images.

MIST Machine Leaning in Cybersecurity Workshop Code Dump Repository

Template alur kerja machine learning.

SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice

Resolución del desafío de la clase cuatro, última clase de la serie Inmersión de Datos de AluraLatam

numpy resources

<h1>Machine Learning With Python: Linear Regression Multiple Variables Sample problem of predicting home price in monroe, new jersey (USA)</h1> Below is the table containing home prices in monroe twp, NJ. Here price depends on area (square feet), bed rooms and age of the home (in years). Given these prices we have to predict prices of new homes …

Text Classification using Machine Learning

ML_binary classification application, docker image created by circleci_deployed to Heroku container

Build and evaluate several machine-learning models to predict credit risk using free data from LendingClub.

This repo contains different models and technique used to classify image in MNIST, CIFSR-10, IRIS Datasets.

This project is about recognizing handwritten digits using custom architecture of Convolutional Neural Networks (CNN). The CNNs have been trained on a dataset of 1.5 million images, resulting in an impressive accuracy of 99.625% on Kaggle.

Performing customer segmentation using the clustering algorithm k-means.