a delightful machine learning tool that allows you to train, test, and use models without writing code

Encodings for neural architecture search

The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.

Training two models, one with with AutoML & one with HyperDrive, compare, and deploy the best model as a service - A Machine Learning Engineer Project

An autoML for explainable text classification.

Machine Learning models for IoT traffic malware detection. (Cybersecurity - Alma Mater Studiorum - University of Bologna)

Pythonizr - Web-based tool to generate Machine Learning boilerplate code

This repository contains an implementation of TPOT for obtaining optimal pipelines with the use of genetic algorithms.

Within a bank’s loan department, a customer’s application undergoes a lot of scrutiny before a decision of approval or rejection is made. The evaluation process can take a while, which opens the possibility of the bank losing a potential customer. To reduce the decision-making time and to increase the accuracy of the decisions being made, we can…

Knowledge-driven AutoML (experiments)

Repository part of Columbia University's MS in Data Science Capstone Project. This repository solves attempts to solve the problem of predicting commercial insurance payments for surgical procedures

Experiments of my bachelor thesis in Computer Science titled Una estrategia de Meta-Learning para flujos genéricos de AutoML

Auto ML application app with dash. Classification and Regression algorithms.

Credit Card Fraud detection system using Kaggle dataset using Azure Automated Machine Learning

This project gave an opportunity to work on annotating data using Appen platform and using Google's AutoML for creating AI models.

🔬AutoML experiments for research and benchmarking purposes

Analyze the tunability of machine learning models with Grid Search, Random Search, and Bayesian Optimization. This project explores hyperparameter tuning methods on diverse datasets, comparing efficiency, stability, and performance. Featuring Random Forest, XGBoost, Elastic Net, and Gradient Boosting.

This project aims to deploy and consume a machine Learning Model generated using AutoML on Azure Machine Learning Studio.

AutoML using PyCaret for credit dataset

AzureML framework train, deploy and consume best model