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30-days-of-javascript

30 days of JavaScript programming challenge is a step-by-step guide to learn JavaScript programming language in 30 days. This challenge may take more than 100 days, please just follow your own pace.

benchm-ml

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

data-science-interviews

Data science interview questions and answers

dgvmtools

R package for processing, analysing and visualising ouput from Dynamic Global Vegetation Models (DGVMs)

geomla

Machine Learning algorithms for spatial and spatiotemporal data

ggtree

:christmas_tree:Visualization and annotation of phylogenetic trees

google-earth-engine-forest-fire-supervised

GEE Fire learning

gsif_tutorials

Global Soil Information Facilities (tutorials)

israd

Repository for the development and release of ISRaD data and tools

pyecharts

🎨 Python Echarts Plotting Library

randomforest-classification

Classifying remote sensing data with random forest

randomforestforremotesensing

rdacca.hp

rdacca.hp: An R packages for performing hierarchical partitioning of constrained ordination

rlpjguess

A R package that wraps the LPJ-GUESS model

us_soilgrids100m

Complete-coverage gridded predictions of soil properties (percent organic carbon, total nitrogen, bulk density, pH, and percent sand and clay) and classes (taxonomic great group and particle size in the control section) for the Conterminous U.S.