{"id":32406559,"url":"https://github.com/marcozanotti/statlearning-course","last_synced_at":"2026-02-25T05:13:11.711Z","repository":{"id":39631160,"uuid":"463072814","full_name":"marcozanotti/statlearning-course","owner":"marcozanotti","description":"Statistical Learning Course","archived":false,"fork":false,"pushed_at":"2024-03-15T17:44:15.000Z","size":12300,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-03-15T18:57:03.923Z","etag":null,"topics":["artificial-intelligence","data-science","deep-learning","machine-learning","statistical-learning","statistics"],"latest_commit_sha":null,"homepage":"https://marcozanotti.netlify.app/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/marcozanotti.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-02-24T08:48:18.000Z","updated_at":"2023-03-24T17:02:31.000Z","dependencies_parsed_at":"2022-09-20T06:52:04.792Z","dependency_job_id":null,"html_url":"https://github.com/marcozanotti/statlearning-course","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/marcozanotti/statlearning-course","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcozanotti%2Fstatlearning-course","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcozanotti%2Fstatlearning-course/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcozanotti%2Fstatlearning-course/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcozanotti%2Fstatlearning-course/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/marcozanotti","download_url":"https://codeload.github.com/marcozanotti/statlearning-course/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marcozanotti%2Fstatlearning-course/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280958218,"owners_count":26420235,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-25T02:00:06.499Z","response_time":81,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","data-science","deep-learning","machine-learning","statistical-learning","statistics"],"created_at":"2025-10-25T12:55:02.933Z","updated_at":"2026-02-25T05:13:11.704Z","avatar_url":"https://github.com/marcozanotti.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\neditor_options: \n  markdown: \n    wrap: 72\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\"\n)\n```\n\n# Statistical Learning, Machine Learning \u0026 Artificial Intelligence\n\nThis course provides a comprehensive introduction to statistical learning, \nmachine learning, and artificial intelligence, bridging traditional statistical \nmodeling with modern algorithmic approaches. Participants will learn how models learn \nfrom data, how to evaluate and interpret results, and how to apply these techniques \nto real-world problems.\n\nThe course teaches how to build end-to-end workflows using **R** production-ready \nframeworks like **tidymodels**, ensuring consistency, reproducibility, and \nscalability from model development to deployment.\n\nBy the end, participants will be able to design, tune, and assess predictive models, \nintegrate them into robust analytical pipelines, and confidently apply \n**state-of-the-art learning techniques** across domains in business, science, and \ntechnology.\n\n- [Website](https://marcozanotti.github.io/statlearning-course/)  \n- [Programme](https://marcozanotti.github.io/statlearning-course/general-infos/statlearn_syllabus.html)  \n\n\n## Materials\n\n- [Lectures](https://github.com/marcozanotti/statlearning-course/tree/master/R)  \n- [Lecture 0 - Tidyverse](https://marcozanotti.github.io/statlearning-course/R/statlearn_lecture0_tidyverse.html)\n\n\n## Suggested References\n\nR Programming:\n\n- [R for Data Science](https://r4ds.had.co.nz/)  \n- [Efficient R Programming](https://csgillespie.github.io/efficientR/index.html)  \n- [R Packages](https://r-pkgs.org/index.html)  \n- [Advanced R](https://adv-r.hadley.nz/)\n\nStatistics \u0026 ML:  \n\n- [Feature Engineering and Selection](https://www.tidymodels.org/books/fes/)  \n- [Tidy Modelling with R](https://www.tmwr.org/)   \n- [Stacking with R](https://stacks.tidymodels.org/index.html)  \n- [AutoML with H2O](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html)  \n- [Deep Learning with Keras](https://keras.rstudio.com/)  \n\nEverything with R:\n\n-   [Big Book of R](https://www.bigbookofr.com/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcozanotti%2Fstatlearning-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarcozanotti%2Fstatlearning-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarcozanotti%2Fstatlearning-course/lists"}