Fast and Accurate ML in 3 Lines of Code
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Updated
May 24, 2024 - Python
Fast and Accurate ML in 3 Lines of Code
Corporate Credit Rating Prediction with AWS SageMaker JumpStart
Traffic analysis for Tor-based malware detection and classification
This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.
predict bike sharing demand using the AWS AutoGloun Framework
This repository shows how to use AWS step functions to train and deploy Autogluon tabular models on Amazon SageMaker
AWS Machine Learning Engineer Nanodegree
This is the repository for the "Predict Bike Sharing Demand with AutoGluon" task as part of the "2. Introduction to Machine Learning" chapter of the AWS Machine Learning Engineer Nanodegree Program on Udacity
TSForecasting - Automated Time Series Forecasting Framework
AutoML Libraries for training multiple ML models in one go with less code.
Kaggle competition - Spectrogram classification
Deploy AutoML models for image classification on AWS Sagemaker with AutoGluon
Automated & Augmented ML Toolbox for Image Classification
Computational experiments for the paper "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost" (IJCNN 2021)
Analysis of machine learning models for credit card fraud detection.
This project uses Tabular Predictions of the AutoGluon library to train several models for the Bike Sharing Demand competition in Kaggle.
Playing with autogluon and some cryptos data
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