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-    "translation_date": "2026-01-16T09:09:29+00:00",
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+  "test-data-predict.8afc47ee7e52874f.webp": {
+    "original_hash": "d9de9b2f4cc312cf745b00a1c3344579",
+    "translation_date": "2026-01-16T09:11:38+00:00",
+    "source_file": "7-TimeSeries/3-SVR/images/test-data-predict.png",
     "language_code": "zh"
   },
-  "apple.c81c8d5965e5e5da.webp": {
-    "original_hash": "ed62bd6204e6efabc031ea19173bafb7",
-    "translation_date": "2026-01-16T09:09:30+00:00",
-    "source_file": "8-Reinforcement/1-QLearning/images/apple.png",
+  "thai-food.c47a7a7f9f05c218.webp": {
+    "original_hash": "d3881195db88ba1cc9fd217f30415da6",
+    "translation_date": "2026-01-16T09:05:34+00:00",
+    "source_file": "4-Classification/images/thai-food.jpg",
     "language_code": "zh"
   },
-  "learned.ed28bcd8484b5287.webp": {
-    "original_hash": "084d4af7881e5b990b53dd562a0aca6e",
-    "translation_date": "2026-01-16T09:09:32+00:00",
-    "source_file": "8-Reinforcement/1-QLearning/images/learned.png",
+  "thai.0269dbab2e78bd38.webp": {
+    "original_hash": "e2b3c8115e57ff80b2c950d3c783e2cf",
+    "translation_date": "2026-01-16T09:05:40+00:00",
+    "source_file": "4-Classification/1-Introduction/images/thai.png",
     "language_code": "zh"
   },
-  "electric-grid.0c21d5214db09ffa.webp": {
-    "original_hash": "d3adac5039330761223c6e9299402b3a",
-    "translation_date": "2026-01-16T09:09:33+00:00",
-    "source_file": "7-TimeSeries/images/electric-grid.jpg",
+  "tokenization.1641a160c66cd2d9.webp": {
+    "original_hash": "568ebd0acfd2a2712e97c2efb889b5b9",
+    "translation_date": "2026-01-16T08:56:54+00:00",
+    "source_file": "6-NLP/2-Tasks/images/tokenization.png",
     "language_code": "zh"
   },
   "train-data-predict.3c4ef4e78553104f.webp": {
@@ -839,16 +851,10 @@
     "source_file": "7-TimeSeries/3-SVR/images/train-data-predict.png",
     "language_code": "zh"
   },
-  "test-data-predict.8afc47ee7e52874f.webp": {
-    "original_hash": "d9de9b2f4cc312cf745b00a1c3344579",
-    "translation_date": "2026-01-16T09:11:38+00:00",
-    "source_file": "7-TimeSeries/3-SVR/images/test-data-predict.png",
-    "language_code": "zh"
-  },
-  "full-data.a82ec9957e580e97.webp": {
-    "original_hash": "e428351ccdd1d7019ab5c486385a0cb1",
-    "translation_date": "2026-01-16T09:11:41+00:00",
-    "source_file": "7-TimeSeries/3-SVR/images/full-data.png",
+  "train-test.8928d14e5b91fc94.webp": {
+    "original_hash": "86bac92c99563659e1acb31bb50bb4e4",
+    "translation_date": "2026-01-16T09:12:17+00:00",
+    "source_file": "7-TimeSeries/2-ARIMA/images/train-test.png",
     "language_code": "zh"
   },
   "train-test.ead0cecbfc341921.webp": {
@@ -857,64 +863,58 @@
     "source_file": "7-TimeSeries/3-SVR/images/train-test.png",
     "language_code": "zh"
   },
-  "full-data-predict.4f0fed16a131c8f3.webp": {
-    "original_hash": "b9ad88a56a196b6b70ffbda06538bca4",
-    "translation_date": "2026-01-16T09:11:51+00:00",
-    "source_file": "7-TimeSeries/3-SVR/images/full-data-predict.png",
-    "language_code": "zh"
-  },
-  "energy-plot.5fdac3f397a910bc.webp": {
-    "original_hash": "ea7eb60322ee54196efdcac12d2313d5",
-    "translation_date": "2026-01-16T09:11:54+00:00",
-    "source_file": "7-TimeSeries/1-Introduction/images/energy-plot.png",
+  "train_progress_raw.2adfdf2daea09c59.webp": {
+    "original_hash": "6b053943a2e7d2b4d7d65460517e854a",
+    "translation_date": "2026-01-16T09:09:20+00:00",
+    "source_file": "8-Reinforcement/2-Gym/images/train_progress_raw.png",
     "language_code": "zh"
   },
-  "scaled.91897dfbaa26ca4a.webp": {
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-    "translation_date": "2026-01-16T09:11:57+00:00",
-    "source_file": "7-TimeSeries/1-Introduction/images/scaled.png",
+  "train_progress_runav.c71694a8fa9ab359.webp": {
+    "original_hash": "4e4c78e37e5b80687dd1d8452fd8ce78",
+    "translation_date": "2026-01-16T09:09:16+00:00",
+    "source_file": "8-Reinforcement/2-Gym/images/train_progress_runav.png",
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   },
-  "currency.e7429812bfc8c608.webp": {
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-    "translation_date": "2026-01-16T09:11:59+00:00",
-    "source_file": "7-TimeSeries/1-Introduction/images/currency.png",
+  "turntable.f2b86b13c53302dc.webp": {
+    "original_hash": "02aa654944ce0872a2a6b41ad6f8bae2",
+    "translation_date": "2026-01-16T08:57:02+00:00",
+    "source_file": "5-Clustering/images/turntable.jpg",
     "language_code": "zh"
   },
-  "july-2014.9e1f7c318ec6d5b3.webp": {
-    "original_hash": "7bee9739087aee3ce5b7157cb92cc043",
-    "translation_date": "2026-01-16T09:12:03+00:00",
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+  "ufo.9e787f5161da9d4d.webp": {
+    "original_hash": "99f743b68eb93ed8c683c0f78ac1a180",
+    "translation_date": "2026-01-16T08:56:14+00:00",
+    "source_file": "3-Web-App/images/ufo.jpg",
     "language_code": "zh"
   },
-  "scaled.e35258ca5cd3d43f.webp": {
-    "original_hash": "789c70fafe6f6dd6e377a90b66e7d740",
-    "translation_date": "2026-01-16T09:12:06+00:00",
-    "source_file": "7-TimeSeries/2-ARIMA/images/scaled.png",
+  "unruly_data.0eedc7ced92d2d91.webp": {
+    "original_hash": "a4a8c088eb459adbbae497322c640811",
+    "translation_date": "2026-01-16T08:53:28+00:00",
+    "source_file": "2-Regression/2-Data/images/unruly_data.jpg",
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+    "original_hash": "6552159e8ea391bd6d60d1ee0ee0a40b",
+    "translation_date": "2026-01-16T08:55:43+00:00",
+    "source_file": "2-Regression/4-Logistic/images/violin.png",
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+  "voronoi.1dc1613fb0439b95.webp": {
+    "original_hash": "561aedc2bbfa5e0af9f4e1b1d5df00a1",
+    "translation_date": "2026-01-16T08:57:20+00:00",
+    "source_file": "5-Clustering/2-K-Means/images/voronoi.png",
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   },
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     "language_code": "zh"
   },
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     "language_code": "zh"
   }
 }
\ No newline at end of file
diff --git a/translations/hk/README.md b/translations/hk/README.md
index 8a1a2efd7..03f389f9f 100644
--- a/translations/hk/README.md
+++ b/translations/hk/README.md
@@ -1,224 +1,230 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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+  "translation_date": "2026-01-16T09:20:33+00:00",
   "source_file": "README.md",
   "language_code": "hk"
 }
 -->
-[![GitHub 授權](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub 貢獻者](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub 問題](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub 拉取請求](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![歡迎 PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)  
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)  
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)  
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)  
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)  
 
-[![GitHub 觀察者](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub 派生](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub 星標](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)  
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)  
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)  
 
 ### 🌐 多語言支援
 
-#### 透過 GitHub Action 支援（自動化且隨時更新）
+#### 透過 GitHub Action 支援（自動化並永遠更新）
 
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語（緬甸）](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](./README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，台灣）](../tw/README.md) | [克羅地亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [意大利語](../it/README.md) | [日語](../ja/README.md) | [卡納達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉雅拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [尼日利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語（法爾西）](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語（Gurmukhi）](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾文）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛文尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [他加祿語（菲律賓語）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md)
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
+<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->  
+[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](./README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)  
+
+> **想本地克隆？**  
+
+> 此倉庫包含超過 50 種語言翻譯，會顯著增加下載大小。若想不帶翻譯文件克隆，請使用稀疏結帳：  
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> 這樣你就能以更快的下載速度獲得完成課程所需的所有內容。  
+<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->  
 
 #### 加入我們的社群
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-我們正舉辦 Discord 的「與 AI 一起學習」系列活動，詳情與加入請到 [Learn with AI Series](https://aka.ms/learnwithai/discord)，活動期間為 2025 年 9 月 18 日至 30 日。你將會學到使用 GitHub Copilot 進行資料科學的技巧與秘訣。
-
-![與 AI 一起學習 系列](../../translated_images/hk/3.9b58fd8d6c373c20.webp)
+我們正在舉辦 Discord 的 AI 學習系列，了解詳情並於 2025 年 9 月 18 日至 30 日加入我們，詳情請見 [Learn with AI Series](https://aka.ms/learnwithai/discord)。你將獲得使用 GitHub Copilot 於資料科學的技巧與秘訣。
 
-# Machine Learning for Beginners - A Curriculum
+![Learn with AI series](../../../../translated_images/hk/3.9b58fd8d6c373c20.webp)
 
-> 🌍 隨著我們透過世界各地文化探索機器學習，一同環遊世界 🌍
+# 機器學習入門 - 課程大綱
 
-Microsoft 的 Cloud Advocates 很高興推出一個為期 12 週、共 26 節課的完整課程，主題為「機器學習」。在本課程中，你將學到有時被稱為「經典機器學習」的內容，主要使用 Scikit-learn 作為函式庫，並避免深度學習（深度學習收錄於我們的 [AI for Beginners' curriculum](https://aka.ms/ai4beginners)）。也可將這些課程與我們的 [『Data Science for Beginners』課程](https://aka.ms/ds4beginners) 搭配學習。
+> 🌍 透過世界各地文化來環遊世界，認識機器學習 🌍
 
-跟我們一起環遊世界，將這些傳統技術應用到來自世界各地的資料中。每一課都包含課前與課後小測、完成課程的書面指示、解答、作業與更多內容。我們以專案為基礎的教學法讓你在構建的同時學習，這是讓新技能更牢固的一種驗證方法。
+微軟的 Cloud Advocates 很高興提供一個為期 12 週、共 26 節課的 **機器學習** 課程大綱。在這個課程中，你會學習到有時稱作 **經典機器學習** 的內容，主要使用 Scikit-learn 這個函式庫，並避開深度學習（深度學習則包含在我們的 [AI for Beginners 課程](https://aka.ms/ai4beginners) 中）。你也可以搭配我們的 ['Data Science for Beginners' 課程](https://aka.ms/ds4beginners) 一起學習！
 
-**✍️ 衷心感謝我們的作者** Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 與 Amy Boyd
+與我們一同遊歷世界，將這些經典技術應用於世界各地的資料。每節課包含課前與課後測驗、書面教學、解答、習題等內容。我們採用以專案為導向的教學法，透過實作學習，確保新技能牢牢掌握。
 
-**🎨 也感謝我們的插畫家** Tomomi Imura、Dasani Madipalli 與 Jen Looper
+**✍️ 衷心感謝作者團隊**Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 及 Amy Boyd
 
-**🙏 特別感謝 🙏 我們的 Microsoft Student Ambassador 的作者、審查者與內容貢獻者**，特別是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila 與 Snigdha Agarwal
+**🎨 亦感謝插畫師**Tomomi Imura、Dasani Madipalli 和 Jen Looper
 
-**🤩 額外感謝 Microsoft Student Ambassadors Eric Wanjau、Jasleen Sondhi 與 Vidushi Gupta 為我們的 R 課程提供協助！**
+**🙏 特別感謝 🙏 微軟學生大使作者、審閱者及內容貢獻者**，尤其是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila 及 Snigdha Agarwal
 
-# 開始使用
+**🤩 額外感謝微軟學生大使 Eric Wanjau、Jasleen Sondhi 和 Vidushi Gupta 貢獻我們的 R 課程！**
 
-請依照下列步驟：
-1. **Fork 該儲存庫**：點選本頁右上角的「Fork」按鈕。
-2. **Clone 該儲存庫**：   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+# 開始
 
-> [在我們的 Microsoft Learn 集合中找到本課程的所有補充資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+請按照以下步驟操作：  
+1. **Fork 倉庫**：點擊本頁右上角的「Fork」按鈕。  
+2. **克隆倉庫**：`git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> 🔧 **需要協助嗎？** 請查看我們的 [Troubleshooting Guide](TROUBLESHOOTING.md) 以取得安裝、設定與執行課程常見問題的解決方法。
+> [你可以在我們的 Microsoft Learn 集合中找到本課程的所有附加資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **需要協助嗎？** 請參考我們的 [疑難排解指南](TROUBLESHOOTING.md)，其中涵蓋安裝、配置及執行課程常見問題的解決方案。
 
-**[學生們](https://aka.ms/student-page)**，要使用這個課程，請將整個 repo fork 到你自己的 GitHub 帳戶，並獨立或與小組一起完成練習：
+**[學生](https://aka.ms/student-page)**，使用此課程請將整個倉庫 fork 至你自己的 GitHub 帳戶，並自行完成練習或組成小組：
 
-- 以課前小測開始。
-- 閱讀講義並完成活動，在每個知識檢查時停下來反思。
-- 嘗試透過理解課程內容來建立專案，而非直接執行解答程式碼；不過每個以專案為導向的課程在 `/solution` 資料夾中都有對應的解答程式碼可供參考。
-- 進行課後小測。
-- 完成挑戰。
-- 完成作業。
-- 在完成一組課程後，請造訪 [討論版](https://github.com/microsoft/ML-For-Beginners/discussions) 並透過填寫適當的 PAT 評分量表來「大聲學習」。PAT 是一種進度評估工具，你可以透過填寫該量表來促進你的學習。你也可以對其他人的 PAT 做出回應，讓我們一起學習。
+- 從課前小測開始。  
+- 閱讀課程並完成活動，於每個知識點靜下心反思。  
+- 嘗試理解課程內容並自行建立專案，而非直接執行解答程式碼；不過，每個專案導向課程的 `/solution` 資料夾中仍有對應的解答程式碼。  
+- 完成課後小測。  
+- 完成挑戰。  
+- 完成作業。  
+- 完成一組課程後，請造訪 [討論區](https://github.com/microsoft/ML-For-Beginners/discussions) 並以「大聲學習」的方式填寫相應的 PAT 打分表。PAT（進度評估工具）是你填寫以自行學習進程的評分標準。你也可以對其他人的 PAT 作出回應，和大家一起進步。
 
-> 若要進一步學習，我們建議遵循這些 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模組與學習路徑。
+> 若想進一步學習，我們推薦以下 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模組與學習路徑。  
 
-**教師們**，我們在 [for-teachers.md](for-teachers.md) 中提供了一些如何使用此課程的建議。
+**教師**，我們也在 [for-teachers.md](for-teachers.md) 提供了如何使用本課程的一些建議。
 
 ---
 
-## 影片導覽
+## 視頻示範
 
-部分課程有短片形式的教學。你可以在各課程內嵌找到這些影片，或於 [Microsoft Developer YouTube 頻道上的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos) 中觀看，點選下方圖片即可前往。
+部分課程有短片教學，可在課程內嵌連結找到，或在 [Microsoft Developer YouTube 頻道的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos) 觀看，點擊下方圖片即可。
 
-[![ML for beginners 橫幅](../../translated_images/hk/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/hk/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## 認識團隊
+## 團隊介紹
 
-[![宣傳影片](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**GIF 作者** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**GIF 動畫由** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) 製作
 
-> 🎥 點選上方圖片以觀看關於專案與創作者的影片！
+> 🎥 點擊上方圖片，觀看本專案及創作者的影片介紹！
 
 ---
 
-## 教學法
-
-我們在建立此課程時選擇了兩個教學原則：確保它是以實作為主的 **專案導向**，並包含 **頻繁的小測驗**。此外，這個課程有一個共同的 **主題**，以維持整體連貫性。
-
-透過確保內容與專案一致，學生在學習過程中會更投入，概念的記憶也會提高。此外，上課前的低壓力小測能夠幫助學生設定學習主題的意圖，而下課後的第二次小測則能進一步鞏固記憶。本課程設計彈性且有趣，可完整修習或分段學習。專案從小型開始，並在 12 週週期結束時逐步增加複雜度。此課程亦包含一段關於機器學習在現實世界應用的後記，可用作額外學分或討論基礎。
-
-> 查閱我們的 [行為準則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯指南](TRANSLATIONS.md) 與 [故障排除指南](TROUBLESHOOTING.md)。我們歡迎你提供建設性的回饋！
-
-## 每堂課包含
-
-- 可選的手繪速記圖
-- 可選的補充影片
-- 影片導覽（僅限部分課程）
-- [課前暖身小測](https://ff-quizzes.netlify.app/en/ml/)
-- 書面課程
-- 對於專案導向的課程，提供逐步的專案構建指南
-- 知識檢查
-- 一項挑戰
-- 補充閱讀
-- 作業
-- [課後小測](https://ff-quizzes.netlify.app/en/ml/)
-
-> **關於語言的一則說明**：這些課程主要以 Python 撰寫，但也有許多以 R 提供。若要完成 R 課程，請前往 `/solution` 資料夾並尋找 R 課程。它們包含 .rmd 副檔名，代表一個 **R Markdown** 檔案，可簡單定義為在 `Markdown document` 中嵌入 `code chunks`（R 或其他語言）與 `YAML header`（用來指示如何格式化輸出，例如 PDF）。因此，它作為資料科學的示範性撰寫架構，允許你將程式碼、其輸出與想法結合起來，並以 Markdown 撰寫。此外，R Markdown 文件可以輸出為 PDF、HTML 或 Word 等格式。
-
-> **關於小測的一則說明**：所有小測都包含在 [Quiz App folder](../../quiz-app) 中，總共有 52 個小測，每個小測有三題。它們在課程中有連結，但也可以在本機執行；請參考 `quiz-app` 資料夾中的說明來在本機托管或部署到 Azure。
-
-| 課程編號 |                             主題                              |                   課程分組                   | 學習目標                                                                                                             |                                                              連結課程                                                               |                        作者                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                機器學習入門                |      [簡介](1-Introduction/README.md)       | 了解機器學習背後的基本概念                                                                                |                                             [課程](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                機器學習的歷史                 |      [簡介](1-Introduction/README.md)       | 了解此領域背後的歷史                                                                                         |                                            [課程](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 公平與機器學習                  |      [簡介](1-Introduction/README.md)       | 在構建和應用機器學習模型時，學生應該考慮哪些關於公平性的主要哲學問題？ |                                              [課程](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                機器學習技術                 |      [簡介](1-Introduction/README.md)       | 機器學習研究人員用甚麼技術來建構模型？                                                                       |                                          [課程](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   回歸分析入門                   |        [回歸](2-Regression/README.md)         | 使用 Python 和 Scikit-learn 入門回歸模型                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                北美南瓜價格 🎃                |        [回歸](2-Regression/README.md)         | 為機器學習準備，視覺化及清理數據                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                北美南瓜價格 🎃                |        [回歸](2-Regression/README.md)         | 建立線性及多項式回歸模型                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                北美南瓜價格 🎃                |        [回歸](2-Regression/README.md)         | 建立邏輯回歸模型                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          網頁應用 🔌                          |           [網頁應用](3-Web-App/README.md)            | 建立一個網頁應用以使用你訓練好的模型                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 分類入門                 |    [分類](4-Classification/README.md)     | 清理、準備及視覺化你的數據；分類導論                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             美味的亞洲與印度菜餚 🍜             |    [分類](4-Classification/README.md)     | 分類器入門                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
-|      12       |             美味的亞洲與印度菜餚 🍜             |    [分類](4-Classification/README.md)     | 更多的分類器                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
-|      13       |             美味的亞洲與印度菜餚 🍜             |    [分類](4-Classification/README.md)     | 使用模型建立推薦系統的網頁應用                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   分群入門                   |        [分群](5-Clustering/README.md)         | 清理、準備及視覺化你的數據；分群導論                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              探索尼日利亞的音樂喜好 🎧              |        [分群](5-Clustering/README.md)         | 探索 K-Means 分群方法                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        自然語言處理入門 ☕️         |   [自然語言處理](6-NLP/README.md)    | 透過建立一個簡單機器人學習自然語言處理的基礎                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      常見的自然語言處理任務 ☕️                      |   [自然語言處理](6-NLP/README.md)    | 透過了解處理語言結構時常見的任務，加深你對自然語言處理的認識                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             翻譯與情感分析 ♥️              |   [自然語言處理](6-NLP/README.md)    | 以珍·奧斯汀作品進行翻譯與情感分析                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  歐洲浪漫酒店 ♥️                  |   [自然語言處理](6-NLP/README.md)    | 以酒店評論進行情感分析 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  歐洲浪漫酒店 ♥️                  |   [自然語言處理](6-NLP/README.md)    | 以酒店評論進行情感分析 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            時間序列預測入門             |        [時間序列](7-TimeSeries/README.md)        | 時間序列預測入門                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ 世界用電量 ⚡️ - 使用 ARIMA 的時間序列預測 |        [時間序列](7-TimeSeries/README.md)        | 使用 ARIMA 進行時間序列預測                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ 世界用電量 ⚡️ - 使用 SVR 的時間序列預測  |        [時間序列](7-TimeSeries/README.md)        | 使用支援向量回歸 (SVR) 的時間序列預測                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             強化學習入門             | [強化學習](8-Reinforcement/README.md) | 以 Q-Learning 介紹強化學習                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 幫助 Peter 避開狼！ 🐺                  | [強化學習](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            真實世界的機器學習情境與應用            |      [ML in the Wild](9-Real-World/README.md)       | 傳統機器學習在真實世界中的有趣且具啟發性的應用                                                               |                                             [課程](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            使用 RAI 儀表板進行機器學習模型除錯          |      [ML in the Wild](9-Real-World/README.md)       | 使用 Responsible AI 儀表板組件進行機器學習模型除錯                                                              |                                             [課程](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [在我們的 Microsoft Learn 集合中尋找此課程的所有其他資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+## 教學方法
+
+我們建立本課程時秉持兩大教學原則：確保它是以**專案導向**為基礎，並包含**頻繁的測驗**。此外，本課程圍繞著一個共同的**主題**，以凝聚整體內容。
+
+確保內容與專案對齊，使學生更投入學習並增強觀念的記憶。此外，上課前的低壓力測驗有助訂立學習目標，課後的第二次測驗則加強記憶。課程設計靈活且有趣，可全程參加或選擇部分學習。專案從小而簡單開始，12 週期末逐漸提升難度。本課程並包含關於機器學習實際應用的後記，可以作為額外學習學分或討論基礎。
+
+> 請參考我們的 [行為守則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯指南](TRANSLATIONS.md)、與 [疑難排解](TROUBLESHOOTING.md)，我們歡迎你的建設性回饋！
+
+## 每節課包括
+
+- 選配速寫筆記  
+- 選配輔助視頻  
+- 視頻示範（部分課程）  
+- [課前暖身測驗](https://ff-quizzes.netlify.app/en/ml/)  
+- 書面課程內容  
+- 專案課程包含逐步指引建置專案  
+- 知識檢測  
+- 挑戰題  
+- 補充閱讀  
+- 作業  
+- [課後測驗](https://ff-quizzes.netlify.app/en/ml/)  
+
+> **關於語言的備註**：本課程主要使用 Python 撰寫，但也有許多課程提供 R 版本。若要完成 R 課程，請到 `/solution` 資料夾尋找 R 課程，文件帶有 .rmd 副檔名，代表 **R Markdown** 文件。R Markdown 是一種結合 `程式碼區塊`（可包含 R 或其他語言）與 `YAML 標頭`（用於格式化輸出，如 PDF）於 `Markdown 文件`中的檔案格式。此架構非常適合數據科學創作，因可同時包含程式碼、輸出結果及書寫的想法。R Markdown 也可以輸出為 PDF、HTML 或 Word 等格式。
+> **有關小測的說明**：所有小測均收錄於 [Quiz App folder](../../quiz-app)，共52個小測，每個含三題。它們會從課程中連結，但小測應用程式可本地執行；請依 `quiz-app` 資料夾中的指示在本地端架設或部署至 Azure。
+
+| Lesson Number |                             主題                              |                   課程分類                   | 學習目標                                                                                                             |                                                              相關課程                                                               |                        作者                        |
+| :-----------: | :------------------------------------------------------------: | :------------------------------------------: | ------------------------------------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
+|      01       |                機器學習簡介                |      [Introduction](1-Introduction/README.md)       | 了解機器學習的基本概念                                                                                       |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|      02       |                機器學習的歷史                 |      [Introduction](1-Introduction/README.md)       | 了解此領域的歷史                                                                                              |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|      03       |                 公平性與機器學習                  |      [Introduction](1-Introduction/README.md)       | 學生應考量建立與應用機器學習模型時，公平性的哲學議題                                                         |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|      04       |                機器學習技術                 |      [Introduction](1-Introduction/README.md)       | 機器學習研究者使用哪種技術來建立模型？                                                                       |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|      05       |                   回歸簡介                   |        [Regression](2-Regression/README.md)         | 開始使用 Python 和 Scikit-learn 建立回歸模型                                                                   |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06       |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 視覺化與清理資料以準備機器學習                                                                                |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07       |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立線性與多項式回歸模型                                                                                      |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
+|      08       |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立邏輯回歸模型                                                                                              |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09       |                          Web App 🔌                          |           [Web App](3-Web-App/README.md)            | 建立一個網頁應用程式以使用你訓練的模型                                                                        |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10       |                 分類簡介                 |    [Classification](4-Classification/README.md)     | 清理、準備及視覺化資料；分類入門                                                                              | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|      11       |             美味的亞洲和印度料理 🍜             |    [Classification](4-Classification/README.md)     | 介紹分類器                                                                                                   | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|      12       |             美味的亞洲和印度料理 🍜             |    [Classification](4-Classification/README.md)     | 更多分類器                                                                                                   | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
+|      13       |             美味的亞洲和印度料理 🍜             |    [Classification](4-Classification/README.md)     | 使用你的模型建立推薦網頁應用程式                                                                              |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14       |                   叢集簡介                   |        [Clustering](5-Clustering/README.md)         | 清理、準備及視覺化資料；叢集入門                                                                              |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15       |              探索奈及利亞音樂品味 🎧              |        [Clustering](5-Clustering/README.md)         | 探索 K-均值叢集法                                                                                             |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|      16       |        自然語言處理簡介 ☕️         |   [Natural language processing](6-NLP/README.md)    | 透過建立簡單機器人學習 NLP 基礎                                                                              |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17       |                      常見的 NLP 工作 ☕️                      |   [Natural language processing](6-NLP/README.md)    | 透過理解處理語言結構時所需的常見任務來深化 NLP 知識                                                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      18       |             翻譯與情感分析 ♥️              |   [Natural language processing](6-NLP/README.md)    | 使用 Jane Austen 文本進行翻譯與情感分析                                                                       |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|      19       |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論進行情感分析 1                                                                                     |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|      20       |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論進行情感分析 2                                                                                     |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|      21       |            時間序列預測簡介             |        [Time series](7-TimeSeries/README.md)        | 時間序列預測入門                                                                                              |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      22       | ⚡️ 世界電力消耗 ⚡️ - 使用 ARIMA 進行時間序列預測 |        [Time series](7-TimeSeries/README.md)        | 使用 ARIMA 進行時間序列預測                                                                                   |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|      23       |  ⚡️ 世界電力消耗 ⚡️ - 使用 SVR 進行時間序列預測  |        [Time series](7-TimeSeries/README.md)        | 使用支持向量回歸 (SVR) 進行時間序列預測                                                                       |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|      24       |             強化學習簡介             | [Reinforcement learning](8-Reinforcement/README.md) | Q-Learning 強化學習入門                                                                                         |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|      25       |                 幫彼得避開狼！🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | 強化學習 Gym                                                                                                   |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  後記   |            現實世界中的機器學習情境與應用            |      [ML in the Wild](9-Real-World/README.md)       | 古典機器學習有趣且啟發性的真實世界應用                                                                        |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         團隊                         |
+|  後記   |            使用 RAI 儀表板進行機器學習模型除錯          |      [ML in the Wild](9-Real-World/README.md)       | 使用 Responsible AI 儀表板元件來進行機器學習模型除錯                                                          |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+
+> [在我們的 Microsoft Learn 集合中找到此課程的所有額外資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## 離線存取
 
-你可以使用 [Docsify](https://docsify.js.org/#/) 離線運行此文件。Fork 此 repo，並在你的本機安裝 [Docsify](https://docsify.js.org/#/quickstart)，然後在此 repo 的根目錄輸入 `docsify serve`。網站將會在你本機的 3000 埠提供服務：`localhost:3000`。
+你可以使用 [Docsify](https://docsify.js.org/#/) 離線瀏覽此說明文件。將本 repo 分叉，於本地機器安裝 [Docsify](https://docsify.js.org/#/quickstart)，接著在本 repo 根目錄輸入 `docsify serve`。網站會在本地主機的 3000 埠上提供服務：`localhost:3000`。
 
 ## PDF
 
-在 [這裡](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) 可以找到附有連結的課程 PDF。
+可在此處找到課程 PDF 並含有連結 [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf)。
 
+## 🎒 其他課程
 
-## 🎒 其他課程 
-
-我們團隊亦製作其他課程！查看：
+我們團隊還有其他課程！快來看看：
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j 初學者](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js 初學者](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 
 ---
 
 ### Azure / Edge / MCP / Agents
-[![AZD 初學者](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Edge AI 初學者](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![MCP 初學者](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents 初學者](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### 生成式 AI 系列
-[![生成式 AI 初學者](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI（.NET）](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-
-[![生成式 AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### 核心學習
-[![機器學習入門](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![資料科學入門](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![人工智能入門](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![網絡安全入門](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![網頁開發入門](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![物聯網入門](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR 開發入門](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Copilot 系列
-[![Copilot（AI 配對程式設計）](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![Copilot（C#/.NET）](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Copilot 冒險](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## 尋求協助
+## 獲得協助
 
-如果於建立 AI 應用程式時遇到困難或有任何疑問。加入學習者及有經驗開發者的討論，一起探討 MCP。這是一個互相支持的社群，歡迎提出問題並自由分享知識。
+如果你遇上困難或對構建 AI 應用程式有任何疑問，歡迎加入 MCP 中的學習者和經驗豐富的開發者討論。這是個支持性的社區，大家樂於提問，並自由分享知識。
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-如果在建立過程中有產品回饋或遇到錯誤，請造訪：
+如果你在構建中有產品反饋或遇到錯誤，請訪問：
 
-[![Microsoft Foundry 開發者論壇](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-免責聲明：
-本文件已使用 AI 翻譯服務 Co-op Translator (https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能仍存在錯誤或不準確之處。以原始語言撰寫的原文應視為具權威性的版本。對於重要資訊，建議採用專業人工翻譯。我們對因使用本翻譯而引致的任何誤解或錯誤詮釋概不負責。
+**免責聲明**：  
+本文件乃使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件以其原文語言版本為準參考。對於重要資訊，建議採用專業人工翻譯。我們不對因使用本翻譯而引起的任何誤解或誤釋承擔責任。
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/mo/README.md b/translations/mo/README.md
index 47d67be64..5b1efe48b 100644
--- a/translations/mo/README.md
+++ b/translations/mo/README.md
@@ -1,223 +1,230 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T10:32:51+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T09:18:48+00:00",
   "source_file": "README.md",
   "language_code": "mo"
 }
 -->
-[![GitHub 授權](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub 貢獻者](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub 問題](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub 拉取請求](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![歡迎 PR](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub 追蹤者](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub 分叉](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub 星標](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 ### 🌐 多語言支援
 
-#### 透過 GitHub Action 支援（自動且保持最新）
+#### 透過 GitHub Action 支援（自動化及持續更新）
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語（Myanmar）](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](../hk/README.md) | [中文（繁體，澳門）](./README.md) | [中文（繁體，台灣）](../tw/README.md) | [克羅地亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [義大利語](../it/README.md) | [日語](../ja/README.md) | [卡納達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉雅拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [奈及利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語（法爾西）](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語（Gurmukhi）](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾字）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛文尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [塔加洛語（菲律賓）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md)
+[阿拉伯文](../ar/README.md) | [孟加拉文](../bn/README.md) | [保加利亞文](../bg/README.md) | [緬甸文 (緬甸)](../my/README.md) | [中文 (簡體)](../zh/README.md) | [中文 (繁體，香港)](../hk/README.md) | [中文 (繁體，澳門)](./README.md) | [中文 (繁體，台灣)](../tw/README.md) | [克羅地亞文](../hr/README.md) | [捷克文](../cs/README.md) | [丹麥文](../da/README.md) | [荷蘭文](../nl/README.md) | [愛沙尼亞文](../et/README.md) | [芬蘭文](../fi/README.md) | [法文](../fr/README.md) | [德文](../de/README.md) | [希臘文](../el/README.md) | [希伯來文](../he/README.md) | [印地文](../hi/README.md) | [匈牙利文](../hu/README.md) | [印尼文](../id/README.md) | [意大利文](../it/README.md) | [日文](../ja/README.md) | [坎納達文](../kn/README.md) | [韓文](../ko/README.md) | [立陶宛文](../lt/README.md) | [馬來文](../ms/README.md) | [馬拉雅拉姆文](../ml/README.md) | [馬拉地文](../mr/README.md) | [尼泊爾文](../ne/README.md) | [奈及利亞皮欽語](../pcm/README.md) | [挪威文](../no/README.md) | [波斯文 (法爾斯文)](../fa/README.md) | [波蘭文](../pl/README.md) | [葡萄牙文 (巴西)](../br/README.md) | [葡萄牙文 (葡萄牙)](../pt/README.md) | [旁遮普文 (古魯穆奇文)](../pa/README.md) | [羅馬尼亞文](../ro/README.md) | [俄文](../ru/README.md) | [塞爾維亞文 (西里爾文)](../sr/README.md) | [斯洛伐克文](../sk/README.md) | [斯洛維尼亞文](../sl/README.md) | [西班牙文](../es/README.md) | [斯瓦希里文](../sw/README.md) | [瑞典文](../sv/README.md) | [他加祿文 (菲律賓語)](../tl/README.md) | [泰米爾文](../ta/README.md) | [泰盧固文](../te/README.md) | [泰文](../th/README.md) | [土耳其文](../tr/README.md) | [烏克蘭文](../uk/README.md) | [烏爾都文](../ur/README.md) | [越南文](../vi/README.md)
+
+> **想本地化複製？**
+
+> 本存儲庫包含超過50種語言的翻譯檔案，因此下載大小大幅增加。若想不下載翻譯而只複製部分，請使用 sparse checkout：
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> 如此能更快速下載，並獲得完成功課所需的一切內容。
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### 加入我們的社群
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-我們正在舉辦 Discord「與 AI 一起學習」系列活動，更多資訊與加入請至 [與 AI 一起學習 系列](https://aka.ms/learnwithai/discord)。活動期間為 2025 年 9 月 18 日至 9 月 30 日。您將會獲得有關在資料科學中使用 GitHub Copilot 的技巧與秘訣。
+我們目前舉辦 Discord AI 學習系列，歡迎於 2025 年 9 月 18 日至 30 日加入並了解詳情：[Learn with AI Series](https://aka.ms/learnwithai/discord)。您將獲得使用 GitHub Copilot 助力資料科學的技巧與秘訣。
 
-![與 AI 一起學習 系列](../../translated_images/mo/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/mo/3.9b58fd8d6c373c20.webp)
 
-# 初學者機器學習課程綱要
+# 初學者的機器學習課程
 
-> 🌍 跟著我們環遊世界，一邊透過世界文化探索機器學習 🌍
+> 🌍 跟隨我們以世界文化為載體探索機器學習 🌍
 
-Microsoft 的 Cloud Advocates 很高興提供一個為期 12 週、共 26 節課的完整課程，主要內容為 **機器學習**。在本課程中，您將學到有時被稱為 **經典機器學習** 的概念，主要使用 Scikit-learn 這個函式庫，並避開深度學習（深度學習已在我們的 [AI 初學者課程](https://aka.ms/ai4beginners) 中介紹）。也可以搭配我們的 [「數據科學初學者」課程](https://aka.ms/ds4beginners) 一起學習！
+微軟的 Cloud Advocates 很高興提供一套為期 12 週、包含 26 節課的**機器學習**課程。在此課程中，您將學習所謂的**經典機器學習**，主要使用 Scikit-learn 函式庫，並避免深度學習（深度學習可參考我們的[初學者人工智能課程](https://aka.ms/ai4beginners)）。同時，也建議搭配我們的[初學者資料科學課程](https://aka.ms/ds4beginners)一起學習！
 
-跟著我們周遊世界，將這些經典技術應用到世界各地的資料。每堂課包含課前與課後的測驗、完成課程所需的書面指示、解答、作業，以及更多內容。我們以專案為導向的教學法讓你在實作中學習，這是讓新技能能夠「養成」的有效方式。
+跟我們一起環遊世界，將這些經典方法應用於各地的資料。每堂課都包含課前與課後小測驗，書面指導、解答、作業及更多。透過以專案為基礎的教學法，讓您在實作中學習，是有效掌握新技能的方式。
 
-**✍️ 衷心感謝作者** Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 以及 Amy Boyd
+**✍️ 衷心感謝我們的作者** Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 與 Amy Boyd
 
-**🎨 也感謝插畫師** Tomomi Imura、Dasani Madipalli 以及 Jen Looper
+**🎨 感謝插畫家** Tomomi Imura、Dasani Madipalli 與 Jen Looper
 
-**🙏 特別感謝 🙏 我們的 Microsoft Student Ambassador 作者、審閱者與內容貢獻者**，尤其是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila，以及 Snigdha Agarwal
+**🙏 特別感謝 🙏 微軟學生大使作者、審稿人及內容貢獻者**，尤其是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila 與 Snigdha Agarwal
 
-**🤩 額外感謝 Microsoft Student Ambassadors Eric Wanjau、Jasleen Sondhi 以及 Vidushi Gupta 對我們的 R 課程所做的貢獻！**
+**🤩 另外特別感謝微軟學生大使 Eric Wanjau、Jasleen Sondhi 與 Vidushi Gupta 對 R 課程的貢獻！**
 
 # 開始使用
 
-請依照下列步驟：
-1. **Fork 倉庫**：點擊此頁面右上角的「Fork」按鈕。
-2. **Clone 倉庫**：   `git clone https://github.com/microsoft/ML-For-Beginners.git`
-
-> [在我們的 Microsoft Learn 集合中找到本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+請按以下步驟操作：
+1. **Fork 本倉庫**：點擊本頁右上方的“Fork”按鈕。
+2. **Clone 倉庫**：`git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> 🔧 **需要協助嗎？** 請查看我們的 [疑難排解指南](TROUBLESHOOTING.md)，以解決安裝、設定及執行課程時常見的問題。
+> [前往 Microsoft Learn 集合查看本課程所有附加資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **需要幫助？** 請參閱我們的[疑難排解指引](TROUBLESHOOTING.md)，獲得安裝、設定及執行課程的常見問題解答。
 
-**[學生](https://aka.ms/student-page)**，要使用此課程，請將整個專案 Fork 到您自己的 GitHub 帳號，並獨立或與小組完成練習：
+**[學生](https://aka.ms/student-page)**，請將本課程完整複製到您自己的 GitHub 帳戶，並自行或組隊完成練習：
 
-- 從課前測驗開始。
-- 閱讀講義並完成活動，於每個知識檢核處暫停並思考。
-- 嘗試透過理解課程內容來建立專案，而不是直接執行解答程式碼；不過每個專案導向課程的解答程式碼都可在 `/solution` 資料夾中找到。
-- 完成課後測驗。
+- 開始先完成課前小測驗。
+- 閱讀課程內容，完成各項活動，並在每個知識檢查點停下來反思。
+- 嘗試理解課程內容而自行建立專案，而非直接執行解答程式碼；不過課程中的 `/solution` 資料夾有針對每個專案導向課程的解答程式碼。
+- 完成課後小測驗。
 - 完成挑戰。
 - 完成作業。
-- 完成一組課程後，請造訪 [討論板](https://github.com/microsoft/ML-For-Beginners/discussions)，並透過填寫相對應的 PAT 評分表來「大聲學習」。PAT 是一份進度評估工具（Progress Assessment Tool），是一種讓你填寫以促進學習的評分規準。您也可以對其他人的 PAT 作出回應，讓大家一起學習。
+- 完成一組課程後，請至[討論板](https://github.com/microsoft/ML-For-Beginners/discussions)分享您的學習成果，填寫相應的 PAT 評分表。PAT（Progress Assessment Tool）是一種幫助您深化學習的評量工具，您也可以對其他人的 PAT 表達反饋，共同成長。
 
-> 若要進一步進修，我們建議追蹤這些 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模組與學習路徑。
+> 如要進一步學習，我們建議接續修習這些[Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 模組與學習路徑。
 
-**教師**，我們已在 [包含一些建議](for-teachers.md) 以說明如何使用本課程。
+**老師們**，我們提供了[使用此課程的建議](for-teachers.md)。
 
 ---
 
 ## 影片導覽
 
-部分課程提供短片形式的教學影片。您可以在每堂課內嵌找到這些影片，或在 Microsoft Developer YouTube 頻道上的 [ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos) 觀看，點選下方圖片即可前往。
+部分課堂提供短影片，您可在課程中直接觀看，也可點擊下方圖片至[微軟開發者 YouTube 頻道 的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos)收看。
 
-[![初學者機器學習 橫幅](../../translated_images/mo/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/mo/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
 ## 認識團隊
 
-[![宣傳影片](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**GIF 由** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**Gif 製作者** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 點擊上方圖片觀看關於專案及其創作者的影片！
+> 🎥 點擊上方圖片，觀看有關本專案及團隊成員的介紹影片！
 
 ---
 
-## 教學法
+## 教學理念
 
-本課程在設計時採用了兩個教學原則：確保以實作為主的 **專案導向**，以及包含 **頻繁測驗**。此外，本課程具有共同的 **主題** 以強化內容的一致性。
+我們在打造此課程時堅持兩個教學原則：確保課程是動手做的**專案型**，且包含**頻繁的小測驗**。此外，本課程具備統一的**主題**，以增加連貫性。
 
-透過讓內容與專案對齊，學生的學習過程會更有參與感，概念的記憶也會增加。此外，課前的低門檻測驗能幫助學員在課堂前就設定學習意圖，而課後的第二次測驗則能加強記憶。本課程設計上彈性且有趣，可整套修習或單元修習。專案由淺入深，至 12 週結束時逐步提升複雜度。本課程亦包含一段關於機器學習真實世界應用的後記，可作為額外加分或討論基礎。
+透過讓內容與專案緊密配合，學習過程更具吸引力，且能加強概念記憶。此外，課前低壓力的測驗幫助學生設定學習目標，課後測驗則促進進一步的知識鞏固。本課程設計靈活有趣，可完整修習或分段學習。專案由簡入繁，12 週結束時達到較高的複雜度。課程包含機器學習實際應用的後記，供當作額外學分或討論基礎。
 
-> 查閱我們的 [行為準則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯指南](TRANSLATIONS.md) 以及 [疑難排解](TROUBLESHOOTING.md)。我們歡迎您的建設性意見！
+> 請查閱我們的[行為準則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯說明](TRANSLATIONS.md)以及[疑難排解](TROUBLESHOOTING.md)。歡迎您提供具建設性的意見回饋！
 
-## 每堂課包含
+## 每堂課內容包括
 
-- 可選的手繪筆記
-- 可選的補充影片
-- 影片導覽（僅部分課程）
-- [課前暖身測驗](https://ff-quizzes.netlify.app/en/ml/)
-- 書面講義
-- 對於專案導向課程，提供逐步指南教你如何建置專案
+- 選項手繪筆記
+- 選項補充影片
+- 影片導覽（部分課程）
+- [課前熱身小測驗](https://ff-quizzes.netlify.app/en/ml/)
+- 書面課程內容
+- 專案課程中，逐步建置專案的詳細指導
 - 知識檢核
-- 一個挑戰
-- 補充閱讀
+- 挑戰題
+- 補充閱讀資料
 - 作業
-- [課後測驗](https://ff-quizzes.netlify.app/en/ml/)
-
-> **關於語言的一則說明**：這些課程主要以 Python 撰寫，但很多課程也提供 R 版本。要完成 R 課程，請前往 `/solution` 資料夾並尋找 R 課程。它們包含 `.rmd` 副檔名，代表一個 **R Markdown** 檔案，可以簡單定義為在 `Markdown` 文件中嵌入 `code chunks`（R 或其他語言）與一個 `YAML header`（用來指導如何輸出為 PDF 等格式）。因此，它是一個示範性的資料科學撰寫框架；您可以結合程式碼、輸出與說明文字，一併以 Markdown 撰寫。R Markdown 文件亦可輸出為 PDF、HTML 或 Word 等格式。
-
-> **關於測驗的一則說明**：所有測驗都集中在 [測驗應用程式資料夾](../../quiz-app)，共 52 個測驗，每個測驗三題。這些測驗由課程內連結，但測驗應用程式也可以在本機執行；請依照 `quiz-app` 資料夾中的說明在本機 host 或部署到 Azure。
-
-| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                                                              Linked Lesson                                                               |                        Author                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                機器學習簡介                |      [簡介](1-Introduction/README.md)       | 學習機器學習背後的基本概念                                                                                |                                             [課程](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                機器學習的歷史                 |      [簡介](1-Introduction/README.md)       | 了解此領域的歷史                                                                                         |                                            [課程](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 公平性與機器學習                  |      [簡介](1-Introduction/README.md)       | 在建立與應用機器學習模型時，學生應該考慮哪些與公平性相關的重要哲學議題？ |                                              [課程](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                機器學習技術                 |      [簡介](1-Introduction/README.md)       | 機器學習研究者使用什麼技術來建立機器學習模型？                                                                       |                                          [課程](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   回歸入門                   |        [Regression](2-Regression/README.md)         | 使用 Python 與 Scikit-learn 開始回歸模型                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                北美洲南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 視覺化並清理資料，以準備進行機器學習                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                北美洲南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立線性與多項式回歸模型                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                北美洲南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立邏輯回歸模型                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          網頁應用程式 🔌                          |           [Web App](3-Web-App/README.md)            | 建立一個網頁應用以使用你訓練好的模型                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 分類入門                 |    [Classification](4-Classification/README.md)     | 清理、準備並視覺化資料；分類入門                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             美味的亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 分類器介紹                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
-|      12       |             美味的亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 更多分類器                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
-|      13       |             美味的亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 使用你的模型建立推薦系統網頁應用程式                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   分群入門                   |        [Clustering](5-Clustering/README.md)         | 清理、準備並視覺化資料；分群入門                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              探索奈及利亞的音樂喜好 🎧              |        [Clustering](5-Clustering/README.md)         | 探索 K-Means 分群方法                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        自然語言處理入門 ☕️         |   [Natural language processing](6-NLP/README.md)    | 透過建立一個簡單機器人來學習自然語言處理的基礎                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      常見的自然語言處理任務 ☕️                      |   [Natural language processing](6-NLP/README.md)    | 透過理解處理語言結構時常用的任務來深化你對自然語言處理的知識                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             翻譯與情感分析 ♥️              |   [Natural language processing](6-NLP/README.md)    | 使用珍·奧斯汀進行翻譯與情感分析                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論進行情感分析 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論進行情感分析 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            時間序列預測入門             |        [Time series](7-TimeSeries/README.md)        | 時間序列預測入門                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ 全球電力使用 ⚡️ - 使用 ARIMA 的時間序列預測 |        [Time series](7-TimeSeries/README.md)        | 使用 ARIMA 的時間序列預測                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ 全球電力使用 ⚡️ - 使用 SVR 的時間序列預測  |        [Time series](7-TimeSeries/README.md)        | 使用 SVR 的時間序列預測                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             強化學習入門             | [Reinforcement learning](8-Reinforcement/README.md) | 以 Q-Learning 為基礎的強化學習介紹                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 幫助 Peter 避免狼！ 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | 強化學習 Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  後記   |            真實世界的機器學習情境與應用            |      [ML in the Wild](9-Real-World/README.md)       | 傳統機器學習在真實世界中的有趣且有啟發性的應用                                                               |                                             [課程](9-Real-World/1-Applications/README.md)                                              |                         團隊                         |
-|  後記   |            使用 RAI 儀表板進行機器學習模型除錯          |      [ML in the Wild](9-Real-World/README.md)       | 使用 Responsible AI 儀表板元件進行機器學習模型除錯                                                              |                                             [課程](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [在我們的 Microsoft Learn 集合中找到本課程的所有其他資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- [課後小測驗](https://ff-quizzes.netlify.app/en/ml/)
+
+> **關於語言的說明**：這些課程主要以 Python 撰寫，但其中許多也有 R 版本。如要完成 R 課程，請至 `/solution` 資料夾尋找以 .rmd 為副檔名的 R 課程，該副檔名表示 **R Markdown** 文件。該文件框架可視為結合了 `程式碼區塊`（R 或其他語言）與 `YAML 標頭`（指引如何格式化輸出成 PDF 等格式）的`Markdown 文件`。因此，R Markdown 是資料科學的優良著作框架，允許您將程式碼、輸出及心得同時記錄在 Markdown 格式。且 R Markdown 文件能輸出為 PDF、HTML 或 Word 等格式。
+> **關於小測的備註**：所有小測試都包含在[Quiz App folder](../../quiz-app) 中，共52個小測， 每個小測有三條問題。它們會在課程中連結，但小測應用程式可以在本地運行；請遵照 `quiz-app` 資料夾中的說明，於本地架設或部署到 Azure。
+
+| Lesson Number |                             主題                              |                   課程分類                   | 學習目標                                                                                                                       |                                                              相關課程                                                               |                        作者                        |
+| :-----------: | :------------------------------------------------------------: | :------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|      01       |                機器學習簡介                                   |      [Introduction](1-Introduction/README.md)       | 學習機器學習的基本概念                                                                                                          |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|      02       |                機器學習歷史                                    |      [Introduction](1-Introduction/README.md)       | 瞭解此領域的歷史背景                                                                                                            |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|      03       |                 公平性與機器學習                               |      [Introduction](1-Introduction/README.md)       | 建構及應用機器學習模型時，學生應該考慮的公平性相關哲學問題                                                                     |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|      04       |                機器學習技術                                   |      [Introduction](1-Introduction/README.md)       | 機器學習研究人員使用哪些技術來建立機器學習模型？                                                                               |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|      05       |                   回歸分析介紹                                |        [Regression](2-Regression/README.md)         | 開始使用 Python 與 Scikit-learn 建構回歸模型                                                                                   |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06       |                北美地區南瓜價格 🎃                             |        [Regression](2-Regression/README.md)         | 在機器學習前進行資料視覺化與清理                                                                                               |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07       |                北美地區南瓜價格 🎃                             |        [Regression](2-Regression/README.md)         | 建立線性與多項式回歸模型                                                                                                       |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
+|      08       |                北美地區南瓜價格 🎃                             |        [Regression](2-Regression/README.md)         | 建立邏輯回歸模型                                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09       |                          網頁應用程式 🔌                      |           [Web App](3-Web-App/README.md)            | 建立一個網頁應用程式來使用您訓練的模型                                                                                         |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|      10       |                 分類介紹                                     |    [Classification](4-Classification/README.md)     | 清理、準備及視覺化資料；分類介紹                                                                                               | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|      11       |             美味的亞洲及印度菜餚 🍜                            |    [Classification](4-Classification/README.md)     | 分類器介紹                                                                                                                    | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|      12       |             美味的亞洲及印度菜餚 🍜                            |    [Classification](4-Classification/README.md)     | 更多分類器                                                                                                                   | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
+|      13       |             美味的亞洲及印度菜餚 🍜                            |    [Classification](4-Classification/README.md)     | 使用模型建構推薦系統網頁應用程式                                                                                            |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|      14       |                   群集分析介紹                               |        [Clustering](5-Clustering/README.md)         | 清理、準備及視覺化資料；群集分析介紹                                                                                           |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15       |              探索奈及利亞音樂喜好 🎧                           |        [Clustering](5-Clustering/README.md)         | 探索K均值群集方法                                                                                                            |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|      16       |        自然語言處理簡介 ☕️                                    |   [Natural language processing](6-NLP/README.md)    | 通過建立簡易機器人來學習 NLP 的基礎知識                                                                                      |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|      17       |                      常見的 NLP 任務 ☕️                      |   [Natural language processing](6-NLP/README.md)    | 深入理解處理語言結構時所需的常見任務                                                                                         |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|      18       |             翻譯與情感分析 ♥️                                 |   [Natural language processing](6-NLP/README.md)    | 使用珍·奧斯汀文本做翻譯與情感分析                                                                                            |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|      19       |                  歐洲浪漫飯店 ♥️                              |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論做情感分析 1                                                                                                    |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|      20       |                  歐洲浪漫飯店 ♥️                              |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論做情感分析 2                                                                                                    |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|      21       |            時間序列預測簡介                                  |        [Time series](7-TimeSeries/README.md)        | 時間序列預測介紹                                                                                                             |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|      22       | ⚡️ 全球電力使用 ⚡️ - 使用 ARIMA 進行時間序列預測             |        [Time series](7-TimeSeries/README.md)        | 使用 ARIMA 進行時間序列預測                                                                                                |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|      23       |  ⚡️ 全球電力使用 ⚡️ - 使用 SVR 進行時間序列預測               |        [Time series](7-TimeSeries/README.md)        | 使用支持向量迴歸器進行時間序列預測                                                                                         |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|      24       |             強化學習介紹                                     | [Reinforcement learning](8-Reinforcement/README.md) | 透過 Q-Learning 介紹強化學習                                                                                               |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|      25       |                 幫助彼得避開大灰狼！🐺                         | [Reinforcement learning](8-Reinforcement/README.md) | 強化學習 Gym                                                                                                               |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  後記         |            真實世界機器學習場景與應用                        |      [ML in the Wild](9-Real-World/README.md)       | 有趣且發人深省的經典機器學習實際應用                                                                                         |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         團隊                         |
+|  後記         |            使用 RAI 儀表板進行機器學習模型偵錯              |      [ML in the Wild](9-Real-World/README.md)       | 使用 Responsible AI 儀表板元件進行機器學習模型偵錯                                                                                   |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+
+> [在我們的 Microsoft Learn 集合中，找到本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## 離線存取
 
-您可以使用 [Docsify](https://docsify.js.org/#/) 離線執行此文件。Fork 此 repo，[安裝 Docsify](https://docsify.js.org/#/quickstart) 在您的本機機器上，然後在此 repo 的根目錄，輸入 `docsify serve`。該網站將在您本機的 3000 連接埠上提供：`localhost:3000`。
+您可以使用 [Docsify](https://docsify.js.org/#/) 來離線執行本文件。Fork 此倉庫，於您的本機安裝 [Docsify](https://docsify.js.org/#/quickstart)，然後在此倉庫根目錄輸入 `docsify serve`。網站將在您的本地主機的 3000 端口提供：`localhost:3000`。
 
-## PDF 檔案
+## PDF
 
-在 [這裡](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) 找到帶連結的課程 PDF。
+在[此處](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf)下載帶連結的課程PDF。
 
+## 🎒 其他課程
 
-## 🎒 其他課程 
-
-Our team produces other courses! Check out:
+我們團隊還製作其他課程！請查看：
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j 新手入門](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js 新手入門](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+[![LangChain4j 初學者](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain.js 初學者](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 
 ---
 
 ### Azure / Edge / MCP / Agents
-[![AZD 新手入門](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Edge AI 新手入門](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![MCP 新手入門](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents 新手入門](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AZD 初學者](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Edge AI 初學者](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![MCP 初學者](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Agents 初學者](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Generative AI Series
-[![生成式 AI 新手入門](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+### 產生式 AI 系列
+[![初學者生成式 AI](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![生成式 AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI（Java）](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI（JavaScript）](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![生成式 AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![生成式 AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### 核心學習
-[![機器學習（初學者）](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![資料科學（初學者）](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![人工智慧（初學者）](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![網絡安全（初學者）](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![網站開發（初學者）](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![物聯網（初學者）](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR 開發（初學者）](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者機器學習](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者資料科學](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者人工智能](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者網絡安全](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![初學者網頁開發](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者物聯網](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者擴增實境開發](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Copilot 系列
-[![AI 配對程式設計的 Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![C#/.NET 的 Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![AI 結對程式設計 Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![C#/.NET Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
 [![Copilot 冒險](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## 取得幫助
+## 尋求協助
 
-如果你遇到困難或對建立 AI 應用有任何疑問，加入其他學習者與有經驗的開發者，在關於 MCP 的討論中互相交流。這是一個互相支持的社群，歡迎提問並自由分享知識。
+如果在構建 AI 應用程式時遇到困難或有任何疑問，歡迎加入學習者和有經驗的開發者，一同討論 MCP。這是個互相支持的社群，問題受到歡迎，知識自由分享。
 
-[![Microsoft Foundry 的 Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-如果你在開發期間有產品回饋或發現錯誤，請造訪：
+如果您在產品使用過程中有反饋或遇到錯誤，請訪問：
 
-[![Microsoft Foundry 開發者論壇](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-免責聲明：
-本文件已使用人工智能翻譯服務 Co-op Translator（https://github.com/Azure/co-op-translator）進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件（其母語版本）應視為具權威性的來源。對於重要資訊，建議採用專業人工翻譯。我們不對因使用本翻譯而導致的任何誤解或曲解承擔責任。
+**免責聲明**：
+本文件係使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們力求準確，但請注意，自動翻譯可能包含錯誤或不精確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議採用專業人工翻譯。我們不對因使用此翻譯而引起的任何誤解或誤譯承擔責任。
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/tw/README.md b/translations/tw/README.md
index 9e592cfdb..75ea46451 100644
--- a/translations/tw/README.md
+++ b/translations/tw/README.md
@@ -1,174 +1,181 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
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-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T10:43:35+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T09:22:16+00:00",
   "source_file": "README.md",
   "language_code": "tw"
 }
 -->
-[![GitHub 授權](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub 貢獻者](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub 問題](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub 拉取請求](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![歡迎 PR](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub 追蹤者](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub 分叉](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub 星標](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 ### 🌐 多語言支援
 
-#### 透過 GitHub Action 支援（自動化且保持最新）
+#### 透過 GitHub Action 支援（自動且隨時更新）
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語 (Myanmar)](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](../hk/README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，臺灣）](./README.md) | [克羅埃西亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [義大利語](../it/README.md) | [日語](../ja/README.md) | [坎納達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉雅拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [尼日利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語（法爾西）](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語（Gurmukhi）](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾字母）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛文尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [他加祿語（菲律賓）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md)
+[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](../hk/README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，台灣）](./README.md) | [克羅埃西亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [義大利語](../it/README.md) | [日語](../ja/README.md) | [卡納達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉雅拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [奈及利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語（法爾西語）](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語（古魯穆奇體）](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾字母）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛維尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [他加祿語（菲律賓語）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md)
+
+> **想要本機複製？**
+
+> 本倉庫包含超過50種語言翻譯，可大幅增加下載大小。若想複製時不含翻譯，請使用稀疏檢出：
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> 這樣你可以以更快的速度取得完成課程所需的所有內容。
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### 加入我們的社群
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-我們有一系列在 Discord 上進行的「與 AI 一起學習」活動，活動期間為 2025 年 9 月 18 日至 30 日。想了解更多並加入我們，請至 [與 AI 一起學習 系列](https://aka.ms/learnwithai/discord)。您將獲得使用 GitHub Copilot 進行資料科學的提示與技巧。
+我們持續推出 Discord AI 學習系列，詳情並加入請見 [Learn with AI Series](https://aka.ms/learnwithai/discord)，時間為 2025 年 9 月 18 日至 30 日。你會學到使用 GitHub Copilot 進行資料科學的小撇步與技巧。
 
-![與 AI 系列](../../translated_images/tw/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/tw/3.9b58fd8d6c373c20.webp)
 
-# 初學者機器學習課程
+# 機器學習入門 - 課程綱要
 
-> 🌍 透過世界各地文化探索機器學習，一同環遊世界 🌍
+> 🌍 透過世界各地文化探索機器學習之旅 🌍
 
-Cloud Advocates at Microsoft 很高興提供一個為期 12 週、共 26 課的 **機器學習** 課程。在這個課程中，您將學習有時被稱為 **經典機器學習** 的內容，主要使用 Scikit-learn 作為函式庫，並避開深度學習（深度學習在我們的 [AI 初學者課程](https://aka.ms/ai4beginners) 中有涵蓋）。也可以將這些課程與我們的 [初學者資料科學課程](https://aka.ms/ds4beginners) 配合使用。
+微軟的 Cloud Advocates 推出為期12週、共26課程的**機器學習**完整課綱。在此課程中，你將學習所謂的**經典機器學習**，主要使用 Scikit-learn 函式庫，並避開深度學習，深度學習部分則收錄於我們的[AI 入門課綱](https://aka.ms/ai4beginners)。此外，建議搭配我們的[資料科學入門課綱](https://aka.ms/ds4beginners)。
 
-跟我們一起環遊世界，將這些經典技術應用到來自世界各地的資料。每堂課都包含課前與課後測驗、書面指示以完成課程、解答、作業等。以專案為基礎的教學法讓您在實作中學習，這是讓新技能更容易「記住」的有效方式。
+隨我們走遍世界，將這些經典技術應用於全球各地的資料。每堂課都有課前及課後測驗、文字教學完成指引、解答、挑戰以及任務。我們的專案導向教學法讓你在實作中學習，是讓新知識穩固掌握的最佳方式。
 
-**✍️ 衷心感謝我們的作者** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ 衷心感謝作者們** Jen Looper、Stephen Howell、Francesca Lazzeri、Tomomi Imura、Cassie Breviu、Dmitry Soshnikov、Chris Noring、Anirban Mukherjee、Ornella Altunyan、Ruth Yakubu 和 Amy Boyd
 
-**🎨 也感謝我們的插畫師** Tomomi Imura, Dasani Madipalli, and Jen Looper
+**🎨 謝謝插畫師** Tomomi Imura、Dasani Madipalli 和 Jen Looper
 
-**🙏 特別感謝 🙏 我們的 Microsoft 學生大使作者、審閱者與內容貢獻者**，尤其是 Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, 與 Snigdha Agarwal
+**🙏 特別感謝🙏 微軟學生大使的作者、審稿人和內容貢獻者**，特別是 Rishit Dagli、Muhammad Sakib Khan Inan、Rohan Raj、Alexandru Petrescu、Abhishek Jaiswal、Nawrin Tabassum、Ioan Samuila 和 Snigdha Agarwal
 
-**🤩 額外感謝 Microsoft 學生大使 Eric Wanjau, Jasleen Sondhi, 與 Vidushi Gupta 為我們的 R 課程所做的貢獻！**
+**🤩 額外感謝微軟學生大使 Eric Wanjau、Jasleen Sondhi 和 Vidushi Gupta 協助我們的 R 課程！**
 
 # 開始使用
 
-請依照以下步驟：
-1. **Fork 本儲存庫**：按本頁右上角的「Fork」按鈕。
-2. **Clone 本儲存庫**：   `git clone https://github.com/microsoft/ML-For-Beginners.git`
-
-> [在我們的 Microsoft Learn 集合中找到本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+請依下列步驟操作：
+1. **Fork 倉庫**：點擊本頁右上角的「Fork」按鈕。
+2. **Clone 倉庫**：`git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> 🔧 **需要幫助嗎？** 請查看我們的 [疑難排解指南](TROUBLESHOOTING.md) 以取得關於安裝、設定和執行課程時常見問題的解決方法。
+> [課程所有額外資源請見我們的 Microsoft Learn 集合](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **需要協助？** 請查看我們的[故障排除指南](TROUBLESHOOTING.md)，內容涵蓋安裝、設定與課程執行的常見問題解決。
 
-**[學生](https://aka.ms/student-page)**，要使用本課程，請將整個 repo Fork 到您自己的 GitHub 帳戶，並自行或與小組一起完成練習：
+**[學生](https://aka.ms/student-page)**，使用本綱要，請 Fork 整個倉庫至你的 GitHub 帳號，並獨立或組隊完成練習：
 
 - 從課前測驗開始。
-- 閱讀講義並完成活動，在每個知識檢核處暫停並反思。
-- 嘗試透過理解課程內容來建立專案，而不是直接執行解答程式碼；不過該程式碼可在每個專案導向課程的 `/solution` 資料夾中找到。
-- 參加課後測驗。
-- 完成挑戰。
+- 閱讀講義並完成活動，每個知識點停下來思考。
+- 嘗試理解課程內容自行建立專案，而非直接執行解決方案程式碼；但解答程式碼可在每個專案導向課程的 `/solution` 資料夾找到。
+- 進行課後測驗。
+- 完成挑戰任務。
 - 完成作業。
-- 完成一組課程後，請造訪 [討論板](https://github.com/microsoft/ML-For-Beginners/discussions) 並透過填寫相應的 PAT 評分規準來「公開學習」。PAT 是一種進度評估工具（Progress Assessment Tool），是一份您填寫以促進學習的評分規準。您也可以對其他人的 PAT 做出回應，讓我們一起學習。
+- 完成一組課程後，請造訪[討論區](https://github.com/microsoft/ML-For-Beginners/discussions)，並透過填寫對應的 PAT 評量工具「大聲學習」。PAT 是個進度評估工具，一種你填寫以促進學習的評分表。你也可以對其他 PAT 作出反應，一起進步。
 
-> 如需進一步學習，我們建議參考這些在 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) 的模組與學習路徑。
+> 若想深入學習，建議跟隨這些[Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott)模組與學習路徑。
 
-**教師**，我們在 [包含一些建議](for-teachers.md) 以說明如何使用本課程。
+**教師們**，我們有[提供一些使用本課程的建議](for-teachers.md)。
 
 ---
 
 ## 影片導覽
 
-部分課程提供短片形式的影片。您可以在課程內容中內嵌觀看，或在 [Microsoft 開發者 YouTube 頻道上的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos) 中觀看，點擊下方圖片即可。
+部分課程有短影片可觀看。可在課程內容中內嵌觀看，或到[Microsoft Developer YouTube 頻道的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos)點擊以下圖片觀看。
 
-[![ML 入門 橫幅](../../translated_images/tw/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/tw/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## 團隊介紹
+## 認識團隊
 
-[![宣傳影片](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**動圖作者** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**GIF 作者** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 點擊上方圖片觀看關於本專案及其創作者的影片！
+> 🎥 點擊上圖觀看有關這個專案及創作者的影片！
 
 ---
 
-## 教學法
+## 教學理念
 
-在建構此課程時，我們選擇了兩項教學原則：確保課程是動手實作的 **專案導向**，並包含 **頻繁的測驗**。此外，本課程具有共通的 **主題**，以增強整體連貫性。
+我們在打造這套課綱時，選擇堅持兩大教學原則：確保課程是實作導向的**專案式學習**，並包含**頻繁測驗**。此外，課程具有統一的**主題**，使內容更有連貫性。
 
-透過確保內容與專案對齊，學習過程對學生而言更具吸引力，概念的記憶也會被強化。此外，課前的低壓力測驗能為學生設定學習某個主題的意圖，而課後的第二次測驗則能確保進一步的記憶。本課程設計具有彈性且充滿趣味，可整體或部分修習。專案從簡單開始，隨著 12 週週期結束逐漸變得更複雜。本課程亦包含一段關於機器學習真實世界應用的附錄，可用作加分或討論的基礎。
+將內容與專案對齊，讓學習過程更投入且概念更加牢固。低門檻的課前小測驗設定學習意圖，課後測驗強化記憶。課程設計靈活且有趣，可全程學完或拆分學習。專案自簡入深，並隨12週進度逐漸複雜。課程附加機器學習實務應用的後記，可以作為加分作業或討論基礎。
 
-> 查看我們的 [行為準則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯](TRANSLATIONS.md) 與 [疑難排解](TROUBLESHOOTING.md) 指南。我們歡迎您的建設性回饋！
+> 可參考我們的[行為準則](CODE_OF_CONDUCT.md)、[貢獻指南](CONTRIBUTING.md)、[翻譯說明](TRANSLATIONS.md)與[故障排除](TROUBLESHOOTING.md)指引。我們歡迎你的建設性回饋！
 
-## 每堂課包含
+## 每堂課程包含
 
-- 可選的草圖筆記
+- 可選的手寫筆記
 - 可選的補充影片
-- 影片導覽（僅限部分課程）
-- [課前暖身測驗](https://ff-quizzes.netlify.app/en/ml/)
-- 書面教學
-- 針對專案導向的課程，提供一步步的專案建置指南
+- 影片導覽（部分課程）
+- [課前暖身小測驗](https://ff-quizzes.netlify.app/en/ml/)
+- 書面講義
+- 專案導向課程的分步指引
 - 知識檢核
-- 一項挑戰
-- 補充閱讀
+- 挑戰任務
+- 補充閱讀材料
 - 作業
 - [課後測驗](https://ff-quizzes.netlify.app/en/ml/)
 
-> **關於語言的說明**：這些課程主要以 Python 撰寫，但許多也提供 R 版本。要完成 R 課程，請前往 `/solution` 資料夾並尋找 R 課程。它們包含 .rmd 副檔名，代表一個 **R Markdown** 檔案，可簡單定義為在 `Markdown document` 中嵌入 `code chunks`（R 或其他語言）與 `YAML header`（指導如何格式化輸出，例如 PDF）。因此，它作為資料科學的範例撰寫框架，允許您結合程式碼、其輸出與想法，並以 Markdown 記錄下來。此外，R Markdown 文件可以輸出為 PDF、HTML 或 Word 等格式。
-
-> **關於測驗的說明**：所有測驗都包含在 [Quiz App 資料夾](../../quiz-app)，共 52 個測驗，每個測驗有三個問題。它們在課程中有連結，但測驗應用程式也可以在本機執行；請依 `quiz-app` 資料夾內的指示在本機託管或部署到 Azure。
-
-| 課程編號 |                             主題                              |                   課程分組                   | 學習目標                                                                                                             |                                                              連結課程                                                               |                        作者                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                機器學習簡介                |      [介紹](1-Introduction/README.md)       | 學習機器學習背後的基本概念                                                                                |                                             [課程](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                機器學習的歷史                 |      [介紹](1-Introduction/README.md)       | 了解此領域背後的歷史                                                                                         |                                            [課程](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 公平性與機器學習                  |      [介紹](1-Introduction/README.md)       | 學生在建立與應用機器學習模型時，應該考量的與公平性相關的重要哲學議題有哪些？ |                                              [課程](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                機器學習的技術                 |      [介紹](1-Introduction/README.md)       | 機器學習研究人員使用哪些技術來建立機器學習模型？                                                                       |                                          [課程](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   迴歸入門                   |        [迴歸](2-Regression/README.md)         | 使用 Python 與 Scikit-learn 開始建立迴歸模型                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                北美南瓜價格 🎃                |        [迴歸](2-Regression/README.md)         | 為機器學習準備資料：視覺化與清理資料                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                北美南瓜價格 🎃                |        [迴歸](2-Regression/README.md)         | 建立線性與多項式迴歸模型                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                北美南瓜價格 🎃                |        [迴歸](2-Regression/README.md)         | 建立邏輯迴歸模型                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          一個 Web 應用程式 🔌                          |           [Web 應用](3-Web-App/README.md)            | 建立一個網頁應用程式來使用你訓練好的模型                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 分類入門                 |    [分類](4-Classification/README.md)     | 清理、準備與視覺化資料；分類介紹                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             美味的亞洲與印度料理 🍜             |    [分類](4-Classification/README.md)     | 分類器介紹                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
-|      12       |             美味的亞洲與印度料理 🍜             |    [分類](4-Classification/README.md)     | 更多分類器                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
-|      13       |             美味的亞洲與印度料理 🍜             |    [分類](4-Classification/README.md)     | 使用你的模型建立推薦系統的網頁應用程式                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   分群入門                   |        [分群](5-Clustering/README.md)         | 清理、準備與視覺化資料；分群介紹                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              探索奈及利亞的音樂喜好 🎧              |        [分群](5-Clustering/README.md)         | 探索 K-Means 分群方法                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        自然語言處理入門 ☕️         |   [自然語言處理](6-NLP/README.md)    | 透過建立簡單機器人來學習 NLP 的基礎                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      常見的 NLP 任務 ☕️                      |   [自然語言處理](6-NLP/README.md)    | 透過理解處理語言結構時常見的任務來加深你對 NLP 的知識                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             翻譯與情感分析 ♥️              |   [自然語言處理](6-NLP/README.md)    | 以 Jane Austen 的作品進行翻譯與情感分析                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  歐洲浪漫旅館 ♥️                  |   [自然語言處理](6-NLP/README.md)    | 使用旅館評論進行情感分析 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  歐洲浪漫旅館 ♥️                  |   [自然語言處理](6-NLP/README.md)    | 使用旅館評論進行情感分析 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            時序預測入門             |        [時間序列](7-TimeSeries/README.md)        | 時間序列預測介紹                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ 世界電力使用 ⚡️ - 使用 ARIMA 的時間序列預測 |        [時間序列](7-TimeSeries/README.md)        | 使用 ARIMA 進行時間序列預測                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ 世界電力使用 ⚡️ - 使用 SVR 的時間序列預測  |        [時間序列](7-TimeSeries/README.md)        | 使用支援向量回歸 (SVR) 進行時間序列預測                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             強化學習入門             | [強化學習](8-Reinforcement/README.md) | 使用 Q-Learning 的強化學習介紹                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 幫助彼得避開狼！ 🐺                  | [強化學習](8-Reinforcement/README.md) | 強化學習 Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  後記   |            真實世界的機器學習情境與應用            |      [真實世界的 ML](9-Real-World/README.md)       | 傳統機器學習在真實世界中有趣且具啟發性的應用                                                               |                                             [課程](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  後記   |            在 ML 中使用 RAI 儀表板進行模型除錯          |      [真實世界的 ML](9-Real-World/README.md)       | 使用 Responsible AI 儀表板元件進行機器學習模型除錯                                                              |                                             [課程](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [在我們的 Microsoft Learn 集合中找到本課程的所有其他資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> **語言說明**：這些課程主要採用 Python 撰寫，但許多課程同時提供 R 語言版本。完成 R 課程，請到 `/solution` 資料夾尋找帶有 .rmd 副檔名的檔案。 .rmd 是**R Markdown**檔案，簡單說就是一份包含 `code chunks`（R 語言或其他語言程式碼）及 `YAML 標頭`（用於格式化輸出，如 PDF）的 `Markdown 文件`。因此，它是資料科學理想的作者框架，允許你結合程式碼、輸出與註記內容以 Markdown 形式書寫。R Markdown 文件還能輸出為 PDF、HTML 或 Word 等格式。
+> **關於測驗的說明**：所有測驗皆收錄於[Quiz App 資料夾](../../quiz-app)，共52個，每個包含三個問題。測驗連結分布於課程中，但測驗應用程式亦可本機執行；請依照 `quiz-app` 資料夾中的說明進行本機託管或部署到 Azure。
+
+| 課程編號 |                             主題                              |                   課程分類                   | 學習目標                                                                                                             |                                                              連結課程                                                               |                        作者                        |
+| :-------: | :----------------------------------------------------------: | :------------------------------------------: | ------------------------------------------------------------------------------------------------------------------- | :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|    01     |                機器學習入門介紹                |      [Introduction](1-Introduction/README.md)       | 學習機器學習的基本概念                                                                                                |                                             [課程](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
+|    02     |                機器學習的歷史                 |      [Introduction](1-Introduction/README.md)       | 了解此領域背後的歷史                                                                                                 |                                            [課程](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
+|    03     |                 公平性與機器學習                  |      [Introduction](1-Introduction/README.md)       | 學生在建立和應用機器學習模型時應考量的公平性相關重要哲學議題                                                        |                                              [課程](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
+|    04     |                機器學習技術                 |      [Introduction](1-Introduction/README.md)       | 機器學習研究人員用來建立機器學習模型的技術有哪些？                                                                  |                                          [課程](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
+|    05     |                   迴歸介紹                   |        [Regression](2-Regression/README.md)         | 使用 Python 及 Scikit-learn 開始建立迴歸模型                                                                          |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|    06     |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 資料視覺化與清理以準備機器學習                                                                                       |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|    07     |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立線性及多項式迴歸模型                                                                                             |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
+|    08     |                北美南瓜價格 🎃                |        [Regression](2-Regression/README.md)         | 建立邏輯迴歸模型                                                                                                     |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|    09     |                          網頁應用程式 🔌                          |           [Web App](3-Web-App/README.md)            | 建立使用已訓練模型的網頁應用程式                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
+|    10     |                 分類介紹                 |    [Classification](4-Classification/README.md)     | 清理、準備及視覺化資料；分類入門                                                                                      | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
+|    11     |             美味亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 分類器介紹                                                                                                           | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau |
+|    12     |             美味亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 更多分類器                                                                                                           | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau |
+|    13     |             美味亞洲與印度料理 🍜             |    [Classification](4-Classification/README.md)     | 使用模型建立推薦網頁應用程式                                                                                          |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
+|    14     |                   聚類介紹                   |        [Clustering](5-Clustering/README.md)         | 清理、準備及視覺化資料；聚類介紹                                                                                       |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|    15     |              探索奈及利亞音樂風格 🎧              |        [Clustering](5-Clustering/README.md)         | 探索 K-平均值聚類方法                                                                                                 |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|    16     |        自然語言處理入門 ☕️         |   [Natural language processing](6-NLP/README.md)    | 透過建立簡單機器人學習自然語言處理基礎                                                                                |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
+|    17     |                      常見 NLP 任務 ☕️                      |   [Natural language processing](6-NLP/README.md)    | 透過理解處理語言結構所需的常見任務，加深 NLP 知識                                                                     |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
+|    18     |             翻譯與情感分析 ♥️              |   [Natural language processing](6-NLP/README.md)    | 使用 Jane Austen 的文本進行翻譯與情感分析                                                                              |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
+|    19     |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論情感分析 1                                                                                                |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
+|    20     |                  歐洲浪漫飯店 ♥️                  |   [Natural language processing](6-NLP/README.md)    | 使用飯店評論情感分析 2                                                                                                |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
+|    21     |            時間序列預測入門             |        [Time series](7-TimeSeries/README.md)        | 時間序列預測入門                                                                                                      |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
+|    22     | ⚡️ 世界用電量 ⚡️ - 使用 ARIMA 的時間序列預測 |        [Time series](7-TimeSeries/README.md)        | 使用 ARIMA 進行時間序列預測                                                                                           |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
+|    23     |  ⚡️ 世界用電量 ⚡️ - 使用 SVR 的時間序列預測  |        [Time series](7-TimeSeries/README.md)        | 使用支持向量回歸 (SVR) 進行時間序列預測                                                                                  |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
+|    24     |             強化學習入門             | [Reinforcement learning](8-Reinforcement/README.md) | 使用 Q-Learning 介紹強化學習                                                                                            |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
+|    25     |                 幫彼得避開狼！🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | 強化學習 Gym                                                                                                          |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
+|  後記   |            真實世界的機器學習場景與應用            |      [ML in the Wild](9-Real-World/README.md)       | 傳統機器學習在真實世界有趣且具啟發性的應用                                                                               |                                             [課程](9-Real-World/1-Applications/README.md)                                              |                         團隊                         |
+|  後記   |            使用 RAI 儀表板進行機器學習模型除錯          |      [ML in the Wild](9-Real-World/README.md)       | 使用 Responsible AI 儀表板元件進行機器學習模型除錯                                                                        |                                             [課程](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+
+> [在我們的 Microsoft Learn 集合中找到此課程的所有其他資源](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## 離線存取
 
-你可以使用 [Docsify](https://docsify.js.org/#/) 來離線執行此文件。Fork 這個 repo，在你的本機安裝 [安裝 Docsify](https://docsify.js.org/#/quickstart)，然後在此 repo 的根資料夾中輸入 `docsify serve`。網站將在本機的 3000 埠提供：`localhost:3000`。
+您可以透過 [Docsify](https://docsify.js.org/#/) 離線運行此文件。複製此倉庫，並在本機安裝 [Docsify](https://docsify.js.org/#/quickstart)，接著在倉庫根目錄輸入 `docsify serve`。網站將在您本機的 3000 端口執行：`localhost:3000`。
 
-## PDF 檔
+## PDF檔案
 
-在 [這裡](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) 找到含連結的課程 PDF。
+在此處可下載帶有連結的課程綱要 PDF：[here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf)。
 
+## 🎒 其他課程
 
-## 🎒 其他課程 
-
-我們團隊也製作其他課程！看看:
+我們團隊也製作其他課程！請查看：
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j 入門](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js 入門](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+[![LangChain4j 初學者](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain.js 初學者](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 
 ---
 
@@ -181,43 +188,43 @@ Cloud Advocates at Microsoft 很高興提供一個為期 12 週、共 26 課的
 ---
  
 ### 生成式 AI 系列
-[![生成式 AI 入門](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的生成式 AI](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![生成式 AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI（Java）](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI（JavaScript）](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+[![生成式 AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![生成式 AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### 核心學習
-[![機器學習入門](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![資料科學入門](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![人工智慧入門](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![網路安全入門](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![網頁開發入門](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![物聯網入門](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR 開發入門](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的機器學習](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的資料科學](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的人工智慧](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的資安](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![初學者的網頁開發](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的物聯網](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![初學者的擴增實境開發](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### Copilot 系列
-[![Copilot（AI 配對程式設計）](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![Copilot（C#/.NET）](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![AI 配對程式設計的 Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![C#/.NET 的 Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
 [![Copilot 冒險](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
-## 取得協助
+## 獲得幫助
 
-如果您在建置 AI 應用程式時遇到困難或有任何問題，請加入其他學習者與資深開發者的討論，共同交流 MCP 的相關議題。這是一個支持性的社群，歡迎提問並自由分享知識。
+如果您卡住或對構建 AI 應用程式有任何疑問，歡迎加入學習者和資深開發者的 MCP 討論社群。這是一個支持性的社群，問題受到歡迎，知識自由共享。
 
-[![Microsoft Foundry Discord 伺服器](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-如果您在建置過程中有產品回饋或發現錯誤，請造訪：
+如果您在構建過程中有產品反饋或遇到錯誤，請訪問：
 
 [![Microsoft Foundry 開發者論壇](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-免責聲明：
-本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應視為具權威性的來源。若內容涉及重要資訊，建議採用專業人工翻譯。我們對因使用此翻譯而導致的任何誤解或錯誤詮釋不負任何法律責任。
+**免責聲明**：  
+本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能包含錯誤或不準確之處。原文文件應視為權威來源。對於重要資訊，建議尋求專業人工翻譯。我們不對因使用本翻譯內容而產生的任何誤解或誤用負責。
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file