{"id":13472307,"url":"https://github.com/mrdbourke/machine-learning-roadmap","last_synced_at":"2026-01-26T21:50:36.828Z","repository":{"id":37573769,"uuid":"278289913","full_name":"mrdbourke/machine-learning-roadmap","owner":"mrdbourke","description":"A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.","archived":false,"fork":false,"pushed_at":"2022-12-08T22:38:52.000Z","size":25983,"stargazers_count":7740,"open_issues_count":12,"forks_count":1168,"subscribers_count":353,"default_branch":"master","last_synced_at":"2025-10-10T14:55:59.560Z","etag":null,"topics":["data","data-science","deep-learning","machine-learning"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mrdbourke.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-07-09T07:07:47.000Z","updated_at":"2025-10-09T11:41:57.000Z","dependencies_parsed_at":"2023-01-25T15:15:23.798Z","dependency_job_id":null,"html_url":"https://github.com/mrdbourke/machine-learning-roadmap","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mrdbourke/machine-learning-roadmap","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrdbourke%2Fmachine-learning-roadmap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrdbourke%2Fmachine-learning-roadmap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrdbourke%2Fmachine-learning-roadmap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrdbourke%2Fmachine-learning-roadmap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mrdbourke","download_url":"https://codeload.github.com/mrdbourke/machine-learning-roadmap/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrdbourke%2Fmachine-learning-roadmap/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28789111,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-26T21:49:50.245Z","status":"ssl_error","status_checked_at":"2026-01-26T21:48:29.455Z","response_time":59,"last_error":"SSL_read: 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":["data","data-science","deep-learning","machine-learning"],"created_at":"2024-07-31T16:00:53.697Z","updated_at":"2026-01-26T21:50:36.806Z","avatar_url":"https://github.com/mrdbourke.png","language":null,"funding_links":[],"categories":["Others","A01_机器学习教程","Learn AI free","🌟 编辑推荐"],"sub_categories":["Machine Learning","学习资源"],"readme":"# 2020 Machine Learning Roadmap (still 90% valid for 2023) \n\n![2020 machine learning roadmap overview](https://raw.githubusercontent.com/mrdbourke/machine-learning-roadmap/master/2020-ml-roadmap-overview.png?token=AD7ZOCOIG7IZXHDL63W6RZK7A3B6I)\n\nA roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.\n\nNamely:\n\n1. 🤔 **Machine Learning Problems** - what does a machine learning problem look like?\n2. ♻️ **Machine Learning Process** - once you’ve found a problem, what steps might you take to solve it?\n3. 🛠 **Machine Learning Tools** - what should you use to build your solution?\n4. 🧮 **Machine Learning Mathematics** - what exactly is happening under the hood of all the machine learning code you're writing?\n5. 📚 **Machine Learning Resources** - okay, this is cool, how can I learn all of this?\n\nSee the [full interactive version](https://dbourke.link/mlmap).\n\n[Watch a feature-length film video walkthrough](https://youtu.be/pHiMN_gy9mk) (yes, really, it's longer than most movies).\n\nMany of the materials in this roadmap were inspired by [Daniel Formoso](https://github.com/dformoso)'s [machine learning mindmaps](https://github.com/dformoso/machine-learning-mindmap),so if you enjoyed this one, go and check out his. He also has a mindmap specifically for [deep learning](https://github.com/dformoso/deeplearning-mindmap) too.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrdbourke%2Fmachine-learning-roadmap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmrdbourke%2Fmachine-learning-roadmap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrdbourke%2Fmachine-learning-roadmap/lists"}