{"id":18565275,"url":"https://github.com/yandexdataschool/mlatimperial2017","last_synced_at":"2026-03-09T08:02:33.617Z","repository":{"id":69129388,"uuid":"78878183","full_name":"yandexdataschool/MLatImperial2017","owner":"yandexdataschool","description":"Materials for the course of machine learning at Imperial College organized by Yandex SDA","archived":false,"fork":false,"pushed_at":"2017-02-09T14:44:07.000Z","size":104881,"stargazers_count":82,"open_issues_count":0,"forks_count":51,"subscribers_count":18,"default_branch":"master","last_synced_at":"2025-04-10T18:57:15.776Z","etag":null,"topics":["deep-learning","imperial-college","keras","lectures","machine-learning","practice","scikit-learn","sklearn","theano","yandex"],"latest_commit_sha":null,"homepage":"","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/yandexdataschool.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}},"created_at":"2017-01-13T19:07:22.000Z","updated_at":"2024-12-18T07:37:28.000Z","dependencies_parsed_at":"2023-03-27T13:05:11.626Z","dependency_job_id":null,"html_url":"https://github.com/yandexdataschool/MLatImperial2017","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/yandexdataschool/MLatImperial2017","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yandexdataschool%2FMLatImperial2017","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yandexdataschool%2FMLatImperial2017/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yandexdataschool%2FMLatImperial2017/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yandexdataschool%2FMLatImperial2017/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yandexdataschool","download_url":"https://codeload.github.com/yandexdataschool/MLatImperial2017/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yandexdataschool%2FMLatImperial2017/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30287447,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-09T02:57:19.223Z","status":"ssl_error","status_checked_at":"2026-03-09T02:56:26.373Z","response_time":61,"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":["deep-learning","imperial-college","keras","lectures","machine-learning","practice","scikit-learn","sklearn","theano","yandex"],"created_at":"2024-11-06T22:18:10.006Z","updated_at":"2026-03-09T08:02:33.591Z","avatar_url":"https://github.com/yandexdataschool.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning, Imperial College London 2017\n\n\n[![Join the chat at https://gitter.im/MLatImperial2017/Lobby](https://badges.gitter.im/MLatImperial2017/Lobby.svg)](https://gitter.im/MLatImperial2017/Lobby?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge\u0026utm_content=badge)\n[![run at everware](https://img.shields.io/badge/run%20me-@everware-blue.svg?style=flat)](https://everware.rep.school.yandex.net/hub/oauth_login?repourl=https://github.com/yandexdataschool/MLatImperial2017.git)\n\nA two-weeks in-depth course of machine learning organized by [Yandex Data School](https://yandexdataschool.com) at Imperial College. Contains theory and **much** practice!\n\nMain topics:\n- python, scientific python (numpy, scipy, matplotlib)\n- python for data science (pandas, sklearn)\n- metric models\n- linear models \n- tree-based models and ensembles, in particular boosting\n- dimensionality reduction\n- tensor computations and neural networks (theano and keras)\n\n## Challenges\n\nThere were two challenges during the course:\n\n- restaraunt reviews classification\n- flavour tagging of B mesons\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyandexdataschool%2Fmlatimperial2017","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyandexdataschool%2Fmlatimperial2017","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyandexdataschool%2Fmlatimperial2017/lists"}