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<Do it! 딥러닝 입문> 도서의 주피터 노트북
A repository to save my machine learning notes.
API4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Wine Recognition API provided by API4AI.
enemy detection in call of Duty :Mobile. Trained on custom dataset.
DeepFovea++: Reconstruction and Super-Resolution for Natural Foveated Rendered Videos (PyTorch).
Speech & Audio Algorithms and Machine Learning Interview Questions
<실무로 통하는 ML 문제 해결 with 파이썬>
Stanford Machine Learning Course
here's code for PCA and it's importance in data reduction to avoid multi-collinearity in dataset.
It is a fully responsive web application that compares two smartphones based on their reviews available on e-commerce sites like Amazon and Flipkart by aspect-based sentiment analysis (ABSA). The application also classifies reviews based on the aspects and corresponding sentiments.
exSeek: extracellular RNA analysis tool for noninvasive biomarker
Sentiment analysis of tweets using machine learning
House Price Prediction can help the customer to arrange the right time to Purchase a House. It is An - ML based Approach which Predicts the Estimated Price of Housing in Mumbai City.
This repository contains classification of documents, to classify documents into one out of several possible malware families, using Google Cloud Platform, PySpark, Jupyter notebook. This project is done for CSCI8360: Data Science Practicum at The University of Georgia.
This repository contains a collection of Python programs designed to demonstrate various machine learning concepts and techniques. Each program focuses on different aspects of machine learning, providing practical examples and implementations.
🚗 Welcome to the "Intrusion Detection using Machine Learning in Automobiles" repository! 🌟 This project is all about enhancing vehicle security using advanced machine learning techniques. We employ algorithms and statistical models to safeguard your car and its precious cargo against unauthorized access and theft.
This is a project that was done for the Skill4U machine Learning program. This project is a Spam email classifier using machine learning. This model uses Gaussian NB algorithm to train the model.
Fishing relevance classifier for youtube videos based on text and metadata
Deplyoing machine learning Web App which utilises OpenAI API to generate image based on text description of the image.
Business analytics, data analysis projects and practices.
I am new to artificial intelligence and this is a program I made to test the similarity of an input to a subject, whose data is input at the beginning. I would like to know wether I got the right idea of a machine learning model here and what changes could be made to make it more accurate.
A light-weight Kaggle challenge to predict crabs' age
Servicio API Rest para el consumo de un Dataset de Videojuegos de Steam. Incluye integración de modelo de regresión de Machine Learning.
The Project repo for Chest X-Ray Pneumonia Detection
This project classifies flower images from the Oxford 102 Dataset using K-means clustering for feature extraction and machine learning for classification. It achieves efficient and accurate classification with optimized feature extraction and reduced feature sets.
Develop a prediction model capable of learning to detect whether a transaction is fraudulent or a genuine purchase.
This repository contains our work for Task 1 of the ITSOLERA internship, a sentiment analysis project. Using Python and Streamlit, We developed a web application that analyzes text input and predicts the sentiment as positive or negative. The repository includes the complete code for the project.
Building a Naive Bayes Classifier using math and numpy from scratch
The code provided is an example of a basic machine learning project. Specifically, it is an implementation of the k-nearest neighbors algorithm to predict the type of iris flower based on user input of the sepal length, sepal width, petal length, and petal width.
This project uses bagging techniques to predict the survival of Titanic passengers. It employs Decision Tree, Bagging, and Random Forest classifiers, trained on passenger attributes like age, gender, ticket class, and fare. The best-performing classifier will be selected for deployment.
Repository of projects from the discipline of data structure II