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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
SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice
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.
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.
Template alur kerja machine learning.
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.
MIST Machine Leaning in Cybersecurity Workshop Code Dump Repository
Resolución del desafío de la clase cuatro, última clase de la serie Inmersión de Datos de AluraLatam
SSE is the basic model of Seach Engine or what people say "Google Search". The working of the model is based on Wikipedia content and the second part of the program use the words from content and use to learn and train the search for better understanding.
Performs sentiment analysis on a given corpus of product reviews.
This repository consist of machine learning models which can be use for predicting the future instance. More specifically this repository is a Machine Learning course for those who are interested in learning the basics of machine learning algorithms.
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.
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.
Text Classification using Machine Learning
This machine learning project predicts house prices based on diverse features, utilizing a dataset containing historical housing data. With organized directories for data, source code, and models, it provides a foundation for accurate predictions and future enhancements. 🏡📈
Word Prediction for augmentative communication using markov model hypothesis
numpy resources
ML_binary classification application, docker image created by circleci_deployed to Heroku container
A notebook to learn the use of CNNs and ShuffleNet
Performing customer segmentation using the clustering algorithm k-means.
Machine-Learning Based Projects