Library for integrated use of H2O with Hyperopt

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

The jupyter notebooks of the deep learning specialization by deeplearning.ai

AutoML Python package with ML models with builtin Hyperparameter Optimization and easy to use API.

Gauging how Support Vector Machine Algorithm behaves with Hyperparameter Tuning

Streamlined machine learning experiment management.

This repository focuses on the Neural Networks and deep learning. It is a workbook you can refer it for a reference. I'll Include following content here: Neurons, perceptrons, weight and biases, learning rate, activation form, hyper parameters, RNN, CNN and other popular concepts.

Evolution-inspired optimisation algorithms

Bayesian optimisation of prophet temperature model parameters with daily and yearly seasonalities plus extra regressors

A simple regression analysis of house prices in USA with 11 features selected on MECE Framework

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

Easy Hyper Parameter Optimization with mlr and mlrMBO.

HOLA: Hyperparameter Optimization, Lightweight Asynchronous

Recommender System. Politecnico di Milano, A.A. 2021-2022

This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.

What important conclusion a company and an employee can take out of Analysis and Predicting Salary