{"id":28304765,"url":"https://github.com/tom-uchida/introduction_to_machine_learning","last_synced_at":"2026-04-28T21:32:28.750Z","repository":{"id":195206397,"uuid":"275500932","full_name":"tom-uchida/Introduction_to_Machine_Learning","owner":"tom-uchida","description":"Machine learning private lesson.","archived":false,"fork":false,"pushed_at":"2020-07-24T13:00:35.000Z","size":635,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-05-31T10:50:40.841Z","etag":null,"topics":["machine-learning","scikit-learn"],"latest_commit_sha":null,"homepage":"https://www.amazon.co.jp/gp/product/4297112094/ref=ppx_yo_dt_b_asin_title_o02_s00?ie=UTF8\u0026psc=1","language":"Python","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/tom-uchida.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":"2020-06-28T03:41:58.000Z","updated_at":"2020-07-24T13:00:38.000Z","dependencies_parsed_at":"2023-09-17T01:22:45.011Z","dependency_job_id":null,"html_url":"https://github.com/tom-uchida/Introduction_to_Machine_Learning","commit_stats":null,"previous_names":["tom-uchida/introduction_to_machine_learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tom-uchida/Introduction_to_Machine_Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tom-uchida%2FIntroduction_to_Machine_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tom-uchida%2FIntroduction_to_Machine_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tom-uchida%2FIntroduction_to_Machine_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tom-uchida%2FIntroduction_to_Machine_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tom-uchida","download_url":"https://codeload.github.com/tom-uchida/Introduction_to_Machine_Learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tom-uchida%2FIntroduction_to_Machine_Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32400861,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-28T19:38:08.556Z","status":"ssl_error","status_checked_at":"2026-04-28T19:37:55.688Z","response_time":56,"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":["machine-learning","scikit-learn"],"created_at":"2025-05-24T01:11:39.873Z","updated_at":"2026-04-28T21:32:28.743Z","avatar_url":"https://github.com/tom-uchida.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## AIエンジニアを目指す人のための機械学習入門\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"./img/cover.jpg\" alt=\"AIエンジニアを目指す人のための機械学習入門\" title=\"AIエンジニアを目指す人のための機械学習入門\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n[書籍 AIエンジニアを目指す人のための機械学習入門（清水琢也・小川雄太郎、技術評論社、20/04/13）](https://gihyo.jp/book/2020/978-4-297-11209-7)\n\n\u003cbr\u003e\n\n### 目次\n\n- 第1章　機械学習の概要と本書の進め方\n  - 1.1　本章の目的と概要\n  - 1.2　機械学習の概要と3つの分類\n  - 1.3　機械学習の各手法の動作原理（アルゴリズム）を学ぶ意義\n  - 1.4　機械学習の勉強方法\n  - 1.5　本書で使用するライブラリと実行環境\n- 第2章　実装しながら学ぶアルゴリズムのこころ・教師あり学習編\n  - 2.1　本章の目的と概要\n  - 2.2　最小二乗法による線形回帰\n  - 2.3　L1正則化，L2正則化による過学習の抑制\n  - 2.4　ロジスティック回帰によるクラス分類\n  - 2.5　SVCによるクラス分類\n  - 2.6　決定木によるクラス分類\n  - 2.7　ランダムフォレストによるクラス分類\n  - 2.8　Naive Bayesによるテキストデータの分類\n- 第3章　実装しながら学ぶアルゴリズムのこころ・教師なし学習編\n  - 3.1　本章の目的と概要\n  - 3.2　主成分分析による次元圧縮\n  - 3.3　k-meansによるクラスタリングとデータ前処理\n  - 3.4　ガウス混合モデル（GMM）によるクラスタリング\n- 第4章　実装しながら学ぶアルゴリズムのこころ・発展編\n  - 4.1　本章の目的と概要\n  - 4.2　勾配ブースティング決定木によるクラス分類\n  - 4.3　エルボー法とシルエット分析によるクラスタ数の探索\n  - 4.4　t-SNEによる次元圧縮（多様体学習）\n  - 4.5　異常検知（Novelty Detection，Outlier Detection）\n- 第5章　機械学習システムの構築フローとモデルの性能評価\n  - 5.1　本章の目的と概要\n  - 5.2　ビジネス理解\n  - 5.3　データ加工\n  - 5.4　モデリング\n  - 5.5　デプロイと運用\n- 付録\n  - A.1　Google Colaboratoryの利用方法\n  - A.2　ローカルPCに機械学習の実装・実行環境を整える方法\n\n\u003cbr\u003e\n\n### 参考リンク\n- https://github.com/shimitaku/MachineLearning-Book","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftom-uchida%2Fintroduction_to_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftom-uchida%2Fintroduction_to_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftom-uchida%2Fintroduction_to_machine_learning/lists"}