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advancedml

antioxs

autohyperbot

blat

Base-level for Active Testing (AT) and Pairwise Curve Comparison (PCC) Meta-learning systems.

building-machine-learning-systems-with-python-third-edition

Code repository for Building Machine Learning Systems with Python Third Edition, by Packt

codehour

Projeto de Ensino 'Hora do Código' para alunos do curso de Engenharia de Computação da UTFPR - Apucarana

datastructures

Data structures' lectures for Computer Engineering course, UTFPR - Apucarana

datastructures2

File data structures' lectures for Computer Engineering course, UTFPR - Apucarana

davinci

Machine Learning models applied for a food quality problem

decisiontreetuninganalysis

Hyperparameter tuning analysis based on HpTuning project results.

deep-learning-with-keras

Code repository for Deep Learning with Keras published by Packt

fungalgrowthclassification

game-using-tables-

there's better ways on creating a table of 2 dimensions but i'll try this anyway

heimdall

hptuning

Machine Learning Hyper-parameter Tuning processes

hyper-sense

Machine Learning experiments checking the default hyper-parameters sensitivity

ia2018uel

idmr

Recommending algorithms of image description via Meta-learning

intelligentsystems1

Artificial Intelligence's lectures for Computer Engineering course, UTFPR - Apucarana

intelligentsystems2

metalearninganalysis

mfeatextractor

MfeatExtractor is an automated code for meta-feature extraction, useful for meta-learning projects.

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

mtlsuite

Meta-learning basic suite for machine learning experiments.

nemenyi

postanalysis

programmingfundamentals

Programming fundamental lectures for undergraduate students

pyhptuning

randombot

Random hyper-parameter exploration for 'OpenML' datasets and 'mlr' classification algorithms

saes2019_datascience