{"id":20512031,"url":"https://github.com/wardlt/machine-learning-intro","last_synced_at":"2026-04-20T15:01:27.325Z","repository":{"id":91621833,"uuid":"438979463","full_name":"WardLT/machine-learning-intro","owner":"WardLT","description":"Notebooks used to introduce scientists to machine learning","archived":false,"fork":false,"pushed_at":"2021-12-16T14:02:34.000Z","size":15847,"stargazers_count":1,"open_issues_count":0,"forks_count":3,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-03-05T22:44:49.110Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","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/WardLT.png","metadata":{"files":{"readme":"README.md","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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-12-16T12:21:27.000Z","updated_at":"2024-03-11T08:00:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"350b8a8f-1d54-44db-b02a-cc2a1ce45603","html_url":"https://github.com/WardLT/machine-learning-intro","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WardLT/machine-learning-intro","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fmachine-learning-intro","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fmachine-learning-intro/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fmachine-learning-intro/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fmachine-learning-intro/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WardLT","download_url":"https://codeload.github.com/WardLT/machine-learning-intro/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WardLT%2Fmachine-learning-intro/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32052534,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T11:35:06.609Z","status":"ssl_error","status_checked_at":"2026-04-20T11:34:48.899Z","response_time":94,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":[],"created_at":"2024-11-15T20:39:26.968Z","updated_at":"2026-04-20T15:01:27.282Z","avatar_url":"https://github.com/WardLT.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction to Machine Learning\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/WardLT/machine-learning-intro/HEAD)\n\nThe corresponding notebooks to the lecture focus on first learning how to use Scikit-Learn, a widely-used machine learning package in Python, \nand then illustrate how to use it with scientific data that requires pre-processing, using molecular property prediction as an example.\n\nThis is a standalone version of the machine learning tutorial from the [ALCF AI Training Series](https://github.com/argonne-lcf/ai-science-training-series/tree/main/01_machineLearning).\n\n## Environment Setup\n\nThere are two ways to run the notebooks.\n\n### Binder\n\nBinder will build the enviornment for you and host it on a cloud-hosted instance. Just click: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/WardLT/machine-learning-intro/HEAD)\n\n### Local Installation\n\nThe `environment.yml` file provided with this README describes how to build the environment with anaconda.\n\nOnce you have anaconda installed, build the environment by calling:\n\n```bash\nconda env create --file environment.yml\n```\n\nfrom the command line. Once installed, follow the instructions Anaconda generates to activate the environment and then launch Jupyter:\n\n```bash\njupyter lab\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fmachine-learning-intro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwardlt%2Fmachine-learning-intro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwardlt%2Fmachine-learning-intro/lists"}