#html #deep_learning #keras_tensorflow #machine_learning #r #workshop

https://github.com/rstudio-conf-2020/dl-keras-tf

#other #accountability #awesome #awesome_list #data_mining #data_science #explainable_ml #fairness #fatml #iml #interpretability #interpretable_ai #interpretable_deep_learning #interpretable_machine_learning #interpretable_ml #machine_learning #machine_learning_interpretability #python #r #transparency #xai

https://github.com/jphall663/awesome-machine-learning-interpretability

#r_lang #awesome #awesome_list #data_analysis #data_science #list #r #rstats

https://github.com/qinwf/awesome-R

#r_lang #brazil #r #rstats #sf #shapefile #spatial #spatial_data

https://github.com/ipeaGIT/geobr

#r_lang

https://github.com/beoutbreakprepared/nCoV2019

#r_lang

https://github.com/openZH/covid_19

#r_lang #bayesian_statistics #branching_process #covid_19 #intervention_study #renewal_process

https://github.com/ImperialCollegeLondon/covid19model

#html #artificial_intelligence #automl #azure_ml #best_practices #deep_learning #demand_forecasting #dilated_cnn #forecasting #hyperparameter_tuning #jupyter_notebook #lightgbm #machine_learning #model_deployment #prophet #python #r #retail #statistical_models #tidyverse #time_series

https://github.com/microsoft/forecasting

#other #analytics #api #artificial_intelligence #aws #cheatsheet #data_science #data_wrangling #database #deep_learning #linux #machine_learning #neural_network #online_course #python #r #reinforcement_learning #scikit_learn #sql #statistics #visualization

https://github.com/tirthajyoti/Data-science-best-resources

#r_lang

https://github.com/rmcelreath/stat_rethinking_2020

#other #bash #data_science #git #manipulation #r #retrieval #sql #study_guide #visualization

https://github.com/shervinea/mit-15-003-data-science-tools

#other #awesome #awesome_list #conformal_prediction #datascience #deeplearning #machine_learning #machinelearning #probability #probability_distribution #probability_distributions #python #r #uncertainty #uncertainty_estimation #uncertainty_quantification

https://github.com/valeman/awesome-conformal-prediction

#r_lang #data_visualization #r

https://github.com/cxli233/FriendsDontLetFriends

#r_lang #democracy #elections #international_relations #national_election_studies #political_datasets #political_institutions #political_parties #survey

https://github.com/erikgahner/PolData

#html #data_science #education #machine_learning #machine_learning_algorithms #machinelearning #machinelearning_python #microsoft_for_beginners #ml #python #r #scikit_learn #scikit_learn_python

Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5].

https://github.com/microsoft/ML-For-Beginners

#typescript #ai_for_science #ai_research #ai_workbench #bioinformatics #claude_science_alternative #desktop_app #electron #linux #local_first #macos #mcp #model_agnostic #open_science #python #r #reproducible_research #research_software #research_workbench #scientific_ai #windows

AIPOCH Open Science is a local-first, open-source AI workbench for research on macOS, Windows, and Linux. It helps you plan, run, and track research in one place with files, code, data tools, reports, and clear proof of how results were made. The benefit to you is simpler, safer, and more repeatable research with less lost work and easier checking of results.

https://github.com/aipoch/open-science