#r_lang

https://github.com/noamross/redoc

#r_lang

https://github.com/rstudio/gt

#r_lang

https://github.com/bbc/bbplot

#r_lang

https://github.com/rnorm/book_sample

#r_lang

https://github.com/rfordatascience/tidytuesday

#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