tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

•    Python

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Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming

yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.

•    Python

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Visual analysis and diagnostic tools to facilitate machine learning model selection. Image by Quatro Cinco, used with permission, Flickr Creative Commons.

machine-learning visual-analysis model-selection visualization scikit-learn visualizer matplotlib estimator residuals transformer advantage anaconda

CV-pretrained-model - A collection of computer vision pre-trained models.

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A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.

data-science image computer-vision deep-learning neural-network mxnet tensorflow model models keras python3 pytorch model-selection image-classification awesome-list object-detection pretrained-models pretrained video-analysis

pathpy - pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

•    Python

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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths observed in an underlying graph topology. Examples for such data include user click streams in information networks, biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, pathpy also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) the package pyTempnets.

temporal-networks pathways analysis sequential-data data data-mining network-analysis networks graph model-selection machine-learning graphical-models multi-order temporal-correlations

IQ-TREE - Efficient phylogenomic software by maximum likelihood

•    C++

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The IQ-TREE software was created as the successor of IQPNNI and TREE-PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid accumulation of phylogenomic data, leading to a need for efficient phylogenomic software that can handle a large amount of data and provide more complex models of sequence evolution. To this end, IQ-TREE can utilize multicore computers and distributed parallel computing to speed up the analysis. IQ-TREE automatically performs checkpointing to resume an interrupted analysis. As input IQ-TREE accepts all common sequence alignment formats including PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a self-readable report file (name suffix .iqtree), a NEWICK tree file (.treefile) which can be visualized by tree viewer programs such as FigTree, Dendroscope or iTOL.

maximum-likelihood phylogenomics phylogenetics mixture-model iq-tree ultrafast-bootstrap model-selection

SVR.jl - Support Vector Regression using libSVM

•    Julia

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Support Vector Regression (SVR) analysis in Julia utilizing the libSVM library. SVR is a module of MADS (Model Analysis & Decision Support).

mads julia support-vector-machine regression model-assessment decision-support machine-learning high-performance-computing model-analysis model-reduction support-vector-regression support-vector-machines uncertainty-quantification sensitivity-analysis model-selection model-simulation model-predictions data-modeling data-analytics data-analysis

Sklearn-genetic-opt - Hyperparameters tuning using evolutionary algorithms

•    Python

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scikit-learn models hyperparameters tuning, using evolutionary algorithms. This is meant to be an alternative from popular methods inside scikit-learn such as Grid Search and Randomized Grid Search.

machine-learning scikit-learn sklearn artificial-intelligence hyperparameters model-selection hyperparameter-optimization evolutionary-algorithms help-wanted looking-for-contributors up-for-grabs genetic-algorithms automl deap contributions-welcome parameter-tuning good-first-issue goodfirstissue begginer-friendly