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diff --git a/translations/kn/README.md b/translations/kn/README.md
index e0c9b341a..66af4259a 100644
--- a/translations/kn/README.md
+++ b/translations/kn/README.md
@@ -1,221 +1,213 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T14:38:11+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T18:04:24+00:00",
   "source_file": "README.md",
   "language_code": "kn"
 }
 -->
-[![GitHub ಪರವಾನಗಿ](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub ಕೊಡುಗಿದಾರರು](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub ಸಮಸ್ಯೆಗಳು](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub ಪುಲ್-ರೆಕ್ವೆಸ್ಟ್‌ಗಳು](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs ಸ್ವಾಗತ](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)
 
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![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/)
 
-### 🌐 ಬಹು-ಭಾಷಾ ಬೆಂಬಲ
+### 🌐 ಬಹು ಭಾಷಾ ಬೆಂಬಲ
 
-#### GitHub Action ಮೂಲಕ ಬೆಂಬಲ (ಸ್ವಯಂಚಾಲಿತ ಮತ್ತು ಸದಾ ತಾಜಾ)
+#### GitHub ಕ್ರಿಯೆಯಿಂದ ಬೆಂಬಲಿಸಲಾಗಿದೆ (ಸ್ವಯಂಚಾಲಿತ ಮತ್ತು ಸದಾ ನವೀಕರಿಸಲಾಗುತ್ತದೆ)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[ಅರೇಬಿಕ್](../ar/README.md) | [ಬೆಂಗಾಳಿ](../bn/README.md) | [ಬಲ್ಗೇರಿಯನ್](../bg/README.md) | [ಬರ್ಮೀಸ್ (ಮ್ಯಾನ್ಮಾರ್)](../my/README.md) | [ಚೈನೀಸ್ (ಸರಳೀಕೃತ)](../zh/README.md) | [ಚೈನೀಸ್ (ಸಾಂಪ್ರದಾಯಿಕ, ಹಾಂಗ್ ಕಾಂಗ್)](../hk/README.md) | [ಚೈನೀಸ್ (ಸಾಂಪ್ರದಾಯಿಕ, ಮಾಕಾವ್)](../mo/README.md) | [ಚೈನೀಸ್ (ಸಾಂಪ್ರದಾಯಿಕ, ತೈವಾನ್)](../tw/README.md) | [ಕ್ರೊಯೇಶಿಯನ್](../hr/README.md) | [ಚೆಕ್](../cs/README.md) | [ಡೆನಿಶ್](../da/README.md) | [ಡಚ್](../nl/README.md) | [ಎಸ್ಟೋನಿಯನ್](../et/README.md) | [ಫಿನ್ನಿಶ್](../fi/README.md) | [ಫ್ರೆಂಚ್](../fr/README.md) | [ಜರ್ಮನ್](../de/README.md) | [ಗ್ರೀಕ್](../el/README.md) | [ಹೀಬ್ರೂ](../he/README.md) | [ಹಿಂದಿ](../hi/README.md) | [ಹಂಗೇರಿಯನ್](../hu/README.md) | [ಇಂಡೋನೇಷಿಯನ್](../id/README.md) | [ಇಟಾಲಿಯನ್](../it/README.md) | [ಜಪಾನೀಸ್](../ja/README.md) | [ಕನ್ನಡ](./README.md) | [ಕೊರಿಯನ್](../ko/README.md) | [ಲಿಥುವೇನಿಯನ್](../lt/README.md) | [ಮಲಯ್](../ms/README.md) | [ಮಲಯಾಳಂ](../ml/README.md) | [ಮರಾಠಿ](../mr/README.md) | [ನೇಪಾಳಿ](../ne/README.md) | [ನೈಜೀರಿಯನ್ ಪಿಡ್ಗಿನ್](../pcm/README.md) | [ನಾರ್ವೀಜಿಯನ್](../no/README.md) | [ಪರ್ಶಿಯನ್ (ಫಾರ್ಸಿ)](../fa/README.md) | [ಪೋಲಿಶ್](../pl/README.md) | [ಪೋರ್ತುಗೀಸ್ (ಬ್ರೆಜಿಲ್)](../br/README.md) | [ಪೋರ್ತುಗೀಸ್ (ಪೋರ್ಚುಗಲ್)](../pt/README.md) | [ಪುಂಜಾಬಿ (ಗುರ್ಮುಖಿ)](../pa/README.md) | [ರೊಮೇನಿಯನ್](../ro/README.md) | [ರಶಿಯನ್](../ru/README.md) | [ಸೆರ್ಬಿಯನ್ (ಸಿರಿಲಿಕ್)](../sr/README.md) | [ಸ್ಲೊವಾಕಿಯನ್](../sk/README.md) | [ಸ್ಲೋವೇನಿಯನ್](../sl/README.md) | [ಸ್ಪಾನಿಷ್](../es/README.md) | [ಸ್ವಾಹಿಲಿ](../sw/README.md) | [ಸ್ವೀಡಿಷ್](../sv/README.md) | [ಟಾಗಾಲೋಗ್ (ಫಿಲಿಪಿನೋ)](../tl/README.md) | [ತಮಿಳು](../ta/README.md) | [ತೆಲುಗು](../te/README.md) | [ಥಾಯ್](../th/README.md) | [ತುರ್ಕಿಶ್](../tr/README.md) | [ಉಕ್ರೇನಿಯನ್](../uk/README.md) | [ಉರ್ದು](../ur/README.md) | [ವಿಯೆಟ್ನಾಮಿ](../vi/README.md)
+[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)](../hk/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](./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)
+
+> **ಸ್ಥಳೀಯವಾಗಿ ಕ್ಲೋನ್ ಮಾಡುವುದು ಇಷ್ಟವೇ?**
+
+> ಈ ರೆಪೋದಲ್ಲಿ 50+ ಭಾಷಾ ಅನುವಾದಗಳು ಸೇರಿವೆ, ಅವು ಡೌನ್‌ಲೋಡ್ ಗಾತ್ರವನ್ನು ಗಮನಾರ್ಹವಾಗಿ ಹೆಚ್ಚಿಸುತ್ತವೆ. ಅನುವಾದಗಳನ್ನು ಇಲ್ಲದೆ ಕ್ಲೋನ್ ಮಾಡಲು, ಸ್ಪಾರ್ಸ್ ಔಟ್‌ಚೆಕ್ ಅನ್ನು ಬಳಸಿ:
+> ```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'
+> ```
+> ಇದರಿಂದ ನಿಮಗೆ ಕೋರ್ಸ್ ಅನ್ನು ತುಂಬಾ ವೇಗವಾಗಿ ಡೌನ್‌ಲೋಡ್ ಮಾಡಿಕೊಳ್ಳಲು ಅಗತ್ಯವಿರುವ ಎಲ್ಲವೂ ಲಭ್ಯವಾಗುತ್ತದೆ.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### ನಮ್ಮ ಸಮುದಾಯದಲ್ಲಿ ಸೇರಿ
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ನಾವು ಡಿಸ್ಕೋರ್ಡ್‌ನಲ್ಲಿ AI ಜೊತೆಗೆ ಕಲಿಕೆ ಸರಣಿ ನಡೆಸುತ್ತಿದ್ದೇವೆ; ಹೆಚ್ಚಿನ ಮಾಹಿತಿಗಾಗಿ ಮತ್ತು 18 - 30 ಸೆಪ್ಟೆಂಬರ್, 2025 ರ ಅವಧಿಯಲ್ಲಿ ನಮ್ಮೊಂದಿಗೆ ಸೇರಲು [AI ಜೊತೆ ಕಲಿಕೆ ಸರಣಿ](https://aka.ms/learnwithai/discord) ಗೆ ಭೇಟಿ ನೀಡಿ. ನೀವು GitHub Copilot ಅನ್ನು ಡೇಟಾ ಸೈನ್ಸ್‌ಗೆ ಬಳಸುವ ಸಲಹೆಗಳು ಮತ್ತು ಟ್ರಿಕ್ಕುಗಳನ್ನು ಪಡೆಯುತ್ತೀರಿ.
+ನಾವು ಡಿಸ್ಕಾರ್ಡ್‌ನಲ್ಲಿ AI ಸರಣಿಯನ್ನು ನಡೆದಿಡುತ್ತಿರುವೆವು, ಇನ್ನಷ್ಟು ತಿಳಿಯಲು ಮತ್ತು 18 - 30 ಸೆಪ್ಟೆಂಬರ್, 2025 ರಲ್ಲಿ ನಮ್ಮೊಂದಿಗೆ ಸೇರಲು [Learn with AI Series](https://aka.ms/learnwithai/discord) ಗೆ ಭೇಟಿ ನೀಡಿ. ಗಿಟ್‌ಹಬ್ ಕೊಪಿಲಾಟ್ ಬಳಸಿ ಡೇಟಾ ಸೈನ್ಸ್‌ಗೆ ಸಲಹೆಗಳು ಮತ್ತು ಟ್ರಿಕ್ಸ್ ಪಡೆಯಲಿದ್ದೀರಿ.
 
-![AI ಜೊತೆ ಕಲಿಕೆ ಸರಣಿ](../../translated_images/kn/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/kn/3.9b58fd8d6c373c20.webp)
 
-# ಆರಂಭಿಕರಿಗಾಗಿ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ - ಪಾಠ್ಯಕ್ರಮ
+# ಆರಂಭಿಕರಿಗಾಗಿ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ - ಒಂದು ಪಠ್ಯಕ್ರಮ
 
-> 🌍 ಪ್ರಪಂಚದ ಸಂಸ್ಕøತಿಗಳ ಮೂಲಕ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಅನ್ವೇಷಿಸುತ್ತಾ ಪ್ರಪಂಚದ ಸುತ್ತ ಪ್ರಯಾಣಿಸಿ 🌍
+> 🌍 ವಿಶ್ವದ ಸಂಸ್ಕೃತಿಗಳನ್ನು ಸಂಧರಿಸಿ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಅನ್ವೇಷಿಸುತ್ತಾ ಜಗತ್ತಿನ ಸುತ್ತಹಾರಣ 🌍
 
-Microsoftನ ಕ್ಲೌಡ್ ಅಡ್ವೊಕೇಟ್‌ಗಳು **ಮೆಷಿನ್ ಲರ್ನಿಂಗ್** ಕುರಿತು 12-ವಾರಗಳ, 26 ಪಾಠಗಳ ಪಾಠ್ಯಕ್ರಮವನ್ನು ನೀಡುತ್ತಿರುವುದಕ್ಕೆ ಸಂತೋಷಪಡುತ್ತವೆ. ಈ ಪಠ್ಯಕ್ರಮದಲ್ಲಿ ನೀವು ಕೆಲವೊಮ್ಮೆ "**ಕ್ಲಾಸಿಕ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್**" ಎಂದು ಕರೆಯುವುವುದನ್ನು ಕಲಿಯುತ್ತೀರಿ, ಮುಖ್ಯವಾಗಿ Scikit-learn ಅನ್ನು ಲೈಬ್ರರಿಯಾಗಿ ಬಳಸುವ ಮೂಲಕ ಮತ್ತು ಡೀಪ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ತಪ್ಪಿಸುತ್ತಾ, ಅದು ನಮ್ಮ [ಆರಂಭಿಕರಿಗಾಗಿ AI ಪಠ್ಯಕ್ರಮ](https://aka.ms/ai4beginners) ನಲ್ಲಿ ಒಳಗೊಂಡಿದೆ. ಈ ಪಾಠಗಳನ್ನು ನಮ್ಮ [ಆರಂಭಿಕರಿಗಾಗಿ ಡೇಟಾ ಸೈನ್ಸ್ ಪಠ್ಯಕ್ರಮ](https://aka.ms/ds4beginners) ಜೊತೆಗೆ ಕೂಡ ಸೇರಿಸಿಕೊಳ್ಳಿ!
+Microsoft ನ ಕ್ಲೌಡ್ ಅಡ್ವೊಕೇಟ್‌ಗಳು 12 ವಾರಗಳ, 26 ಪಾಠಗಳ ಪಠ್ಯಕ್ರಮವನ್ನು ಒದಗಿಸಲು ಸಂತೋಷ ಪಡುತ್ತಿದ್ದರು, ಇದು **ಮೆಷಿನ್ ಲರ್ನಿಂಗ್** ಕುರಿತು ಎಲ್ಲವನ್ನೂ ಒಳಗೊಂಡಿದೆ. ಈ ಪಠ್ಯಕ್ರಮದಲ್ಲಿ, ಕೆಲವು ವೇಳೆ **ಕ್ಲಾಸಿಕ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯಲ್ಪಡುವುದನ್ನು ನೀವು ಕಲಿಯುತ್ತೀರಿ, ಇದರಲ್ಲಿ ಮುಖ್ಯವಾಗಿ ಸ್ಕೈಕಿಟ್-ಲರ್ನ್ ಲೈಬ್ರರಿ ಬಳಸಲ್ಪಟ್ಟಿದೆ ಮತ್ತು ನಾವು ಡೀಪ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ದೂರವಿಡುತ್ತೇವೆ, ಅದು ನಮ್ಮ [AI for Beginners' curriculum](https://aka.ms/ai4beginners) ನಲ್ಲಿ ಒಳಗೊಂಡಿದೆ. ಈ ಪಾಠಗಳನ್ನು ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) ಜೊತೆಗೆ ಬಳಸಬಹುದು!
 
-ನಮ್ಮೊಂದಿಗೆ ಪ್ರಪಂಚದ ಸುತ್ತ ಯಾತ್ರೆ ಮಾಡಿ, ನಾವು ಈ ಪರಂಪರাগত ತಂತ್ರಗಳನ್ನು ಜಗತ್ತಿನ ವಿವಿಧ ಭಾಗಗಳಿಂದ ಬಂದ ಡೇಟಾದ ಮೇಲೆ ಅನ್ವಯಿಸುತ್ತೇವೆ. ಪ್ರತಿ ಪಾಠದಲ್ಲಿ ಪೂರ್ವ ಮತ್ತು ಪೋಸ್ಟ್-ಪಾಠ ಕ್ವಿಜ್‌ಗಳು, ಪಾಠವನ್ನು ಪೂರ್ಣಗೊಳಿಸಲು ಲೇಖಿತ ಸೂಚನೆಗಳು, ಒಂದು ಪರಿಹಾರ, ಒಂದು ನಿಯುಕ್ತಿ ಮತ್ತು ಇನ್ನಷ್ಟು ಇರುತ್ತದೆ. ನಮ್ಮ ಪ್ರಾಜೆಕ್ಟ್-ಆಧಾರಿತ ಪಠ್ಯಶೈಲಿ ನಿಮಗೆ ನಿರ್ಮಿಸಲು ಕರೆಯಾಗುವಂತೆ ಕಲಿತುಕೊಳ್ಳಲು ಅವಕಾಶವನ್ನು ಒದಗಿಸುತ್ತದೆ — ಹೊಸ ಕೌಶಲ್ಯಗಳು ನೆಲೆಯ ಮೇಲೆ ಉಳಿಯುವ ರಚನಾತ್ಮಕ ವಿಧಾನ.
+ಮಹಾಕಾಶದಲ್ಲಿರುವ ಯಾವುದೇ ಡೇಟಾವನ್ನು ಹಲವಾರು ಭಾಗಗಳಿಂದ ನಾವು ಅನ್ವಯಿಸುವ ಕಾರಣದಿಂದಾಗಿ ನೀವು ನಮ್ಮೊಂದಿಗೆ ಜಗತ್ತಿನ ಸುತ್ತ ಪ್ರಯಾಣ ಮಾಡುತ್ತೀರಿ. ಪ್ರತಿ ಪಾಠವು ಪೂರ್ವ ಮತ್ತು ನಂತರದ ಕ್ವಿಜ್‌ಗಳು, ಬರಹದ ಮೆಳಕುಗಳು, ಪಾಠವನ್ನು ಮುಗಿಸಲು ಪರಿಹಾರ, ನಿಯೋಜನೆ, ಮತ್ತು ಇನ್ನೂ ಹೆಚ್ಚು ಹೊಂದಿದೆ. ನಮ್ಮ ಯೋಜನೆಾಧಾರಿತ ಶಿಕ್ಷಣಶೀಲ ಧೋರಣೆಯ ಮೂಲಕ ನೀವು ಕಲಿಯುತ್ತಾ ನಿರ್ಮಿಸುತ್ತೀರಿ, ಇದು ಹೊಸ ಕೌಶಲಗಳನ್ನು 'ನಂಚಿಕೊಳ್ಳುವ' ಪ್ರಮಾಣಿತ ವಿಧಾನ.
 
-**✍️ ನಮ್ಮ ಲೇಖಕರಿಗೆ ಹೃತ್ಪೂರ್ವಕ ಧನ್ಯವಾದಗಳು** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu ಮತ್ತು Amy Boyd
+**✍️ ನಮ್ಮ రచನೆಗಾರರಿಗೆ ಹೃದಯಪೂರ್ವಕ ಧನ್ಯವಾದಗಳು** ಜೆನ್ ಲೂಪರ್, ಸ್ಟೀಫನ್ ಹೌಲ್, ಫ್ರಾನ್ಸೆಸ್ಕಾ ಲಾಜೆರೀ, ಟೊಮೊಮಿ ಇಮುರು, ಕ್ಯಾಸಿ ಬ್ರೇವಿಯು, ಡ್ಮಿಟ್ರಿ ಸೋಷ್ನಿಕೊವ್, ಕ್ರಿಸ್ ನೋರಿಂಗ್, ಅನಿರ್ಬಾನ್ ಮುಖರ್ಜೀ, ಓರ್ನೆಲ್ಲಾ ಅಲ್ಟುನಿಯನ್, ರೂತ್ ಯಾಕುಬು ಮತ್ತು ಆಮಿ ಬಾಯ್ಡ್
 
-**🎨 ನಮ್ಮ ಚಿತ್ರಕಾರರಿಗೆ ಧನ್ಯವಾದಗಳು** Tomomi Imura, Dasani Madipalli, ಮತ್ತು Jen Looper
+**🎨 ನಮ್ಮ ಚಿತ್ರಕಾರರಿಗೂ ಧನ್ಯವಾದಗಳು** ಟೊಮೊಮಿ ಇಮುರು, ದಾಸನಿ ಮಾದಿಪಳ್ಳಿ ಮತ್ತು ಜೆನ್ ಲೂಪರ್
 
-**🙏 ವಿಶೇಷ ಧನ್ಯವಾದಗಳು 🙏 ನಮ್ಮ Microsoft Student Ambassador ಲೇಖಕರು, ಸಮೀಕ್ಷಕರು ಮತ್ತು ವಿಷಯದ ಕೊಡುಗೆದಾರರಿಗೆ**, ವಿಶೇಷವಾಗಿ Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, ಮತ್ತು Snigdha Agarwal
+**🙏 ನಮ್ಮ Microsoft ವಿದ್ಯಾರ್ಥಿ ಮೊಳಗೋಳಿಕ ರಚನೆಗಾರರು, ವಿಮರ್ಶಕರು ಮತ್ತು ವಿಷಯದ ಸಹಯೋಗಿಗಳಿಗೆ ವಿಶೇಷ ಧನ್ಯವಾದಗಳು**, ವಿಶೇಷವಾಗಿ ರಿಷಿತ್ ಡಾಗ್ಲಿ, ಮುಹಮ್ಮದ್ ಸಕಿಬ್ ಖಾನ್ ಇನಾನ್, ರೋಹನ್ ರಾಜ್, ಅಲೆಕ್ಸಾಂಡ್ರು ಪೆಟ್ರೆಸ್ಕು, అభಿಷೇಕ్ జೈಸ్వాల్, ನವ್ರಿನ್ ತಬಸ್ಸುಂ, ಇಒನ್ ಸಮುಲಿಯಾ ಮತ್ತು ಸ್ನಿಗ್ಧಾ ಅಗರವಲ್
 
-**🤩 ನಮ್ಮ R ಪಾಠಗಳಿಗಾಗಿ Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, ಮತ್ತು Vidushi Gupta ಅವರಿಗೂ ಹೆಚ್ಚುವರಿ ಕೃತಜ್ಞತೆ!**
+**🤩 ನಮ್ಮ R ಪಾಠಗಳಿಗೆ Microsoft ವಿದ್ಯಾರ್ಥಿ ಮೊಳಗೋಳಿಕರಾದ ಎರಿಕ್ ವಾನ್ಜೌ, ಜಸ್‌ಲೀನ್ ಸಂದಿ ಮತ್ತು ವಿದ್ಯುಷಿ ಗುಪ್ತಾಗೆ ಹೆಚ್ಚುವರಿ ಧನ್ಯವಾದಗಳು!**
 
 # ಪ್ರಾರಂಭಿಸುವುದು
 
 ಈ ಹಂತಗಳನ್ನು ಅನುಸರಿಸಿ:
-1. **ರಿಪೊಸಿಟರಿಯನ್ನು Fork ಮಾಡಿ**: ಈ ಪುಟದ ಮೇಲ್ಭಾಗದ ಬಲಕ್ಕೂ ಮೂಕದಲ್ಲಿ ಇರುವ "Fork" ಬಟನ್ ಮೇಲೆ ಕ್ಲಿಕ್ ಮಾಡಿ.
+1. **ರಿಪೊಸಿಟರಿಯನ್ನು ಫೋರ್ಕ್ ಮಾಡಿ**: ಈ ಪುಟದ ಮೇಲೆ ಬಲ-ಮೇಲ್ಭಾಗದಲ್ಲಿ ಇರುವ "Fork" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ.
 2. **ರಿಪೊಸಿಟರಿಯನ್ನು ಕ್ಲೋನ್ ಮಾಡಿ**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [ಈ ಕೋರ್ಸ್‍ಗಾಗಿ ಎಲ್ಲಾ ಹೆಚ್ಚುವರಿ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಮ್ಮ Microsoft Learn ಸಂಗ್ರಹದಲ್ಲಿ ಕಂಡುಹಿಡಿಯಿರಿ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
-
-> 🔧 **ಸಹಾಯ ಬೇಕೇ?** ಸ್ಥಾಪನೆ, ಸೆಟ್‌ಅಪ್ ಮತ್ತು ಪಾಠಗಳನ್ನು ನಡೆಸುವ ಸಾಮಾನ್ಯ ಸಮಸ್ಯೆಗಳಿಗೆ ಪರಿಹಾರಗಳಿಗೆ ನಮ್ಮ [ತೊಂದರೆ ಪರಿಹಾರ ಮಾರ್ಗದರ್ಶಿ](TROUBLESHOOTING.md) ಅನ್ನು ನೋಡಿ.
+> [ಈ ಕೋರ್ಸ್‌ನ ಎಲ್ಲ ಹೆಚ್ಚುವರಿ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಮ್ಮ Microsoft Learn ಕಲಕ್ಷನ್‌ನಲ್ಲಿ ಕಂಡುಹಿಡಿಯಿರಿ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **ಸಹಾಯ ಬೇಕೇ?** ಸ್ಥಾಪನೆ, ಸೆಟ್‌ಅಪ್ ಮತ್ತು ಪಾಠಗಳನ್ನು ನಡೆಸುವುದು ಸಂಬಂಧಿಸಿದ ಸಾಮಾನ್ಯ ಸಮಸ್ಯೆಗಳ ಪರಿಹಾರಗಳಿಗೆ [Troubleshooting Guide](TROUBLESHOOTING.md) ಪರಿಶೀಲಿಸಿ.
 
-**[ವಿದ್ಯಾರ್ಥಿಗಳು](https://aka.ms/student-page)**, ಈ ಪಾಠ್ಯಕ್ರಮವನ್ನು ಬಳಸಲು, ಸಂಪೂರ್ಣ ರೆಪೋವನ್ನು ನಿಮ್ಮದೇ GitHub ಖಾತೆಗೆ Fork ಮಾಡಿ ಮತ್ತು ವ್ಯಾಯಾಮಗಳನ್ನು ನೀವು ತಾನೇ ಅಥವಾ ಗುಂಪಿನಲ್ಲಿ ಪೂರ್ಣಗೊಳಿಸಿ:
+**[ವಿದ್ಯಾರ್ಥಿಗಳು](https://aka.ms/student-page)**, ಈ ಪಠ್ಯಕ್ರಮವನ್ನು ಬಳಸಲು, ಪ್ರತಿಯೊಂದು ಯೂಸರ್ ಖಾತೆಗೆ ಸಂಪೂರ್ಣ ರೆಪೊ ಫೋರ್ಕ್ ಮಾಡಿ ಮತ್ತು ವ್ಯಾಯಾಮಗಳನ್ನು ಸ್ವತಃ ಅಥವಾ ಗುಂಪಿನಿಂದ ಪೂರ್ಣಗೊಳಿಸಿ:
 
-- ಪೂರ್ವ-ಉಪನ್ಯಾಸ ಕ್ವಿಜ್‌ನಿಂದ ಪ್ರಾರಂಭಿಸಿ.
-- ಉಪನ್ಯಾಸವನ್ನು ಓದಿ ಮತ್ತು ಚಟುವಟಿಕೆಗಳನ್ನು ಪೂರ್ಣಗೊಳಿಸಿ, ಪ್ರತಿ ಜ್ಞಾನ ಪರಿಶೀಲನೆಯಲ್ಲೂ ನಿಂತು ಪರಾವಲೋಕನ ಮಾಡಿ.
-- ಪರಿಹಾರ ಕೋಡ್ ಅನ್ನು ದೌರ್ಬಲ್ಯವಿಲ್ಲದೆ ಓಡಿಸುವ ಬದಲು ಪಾಠಗಳನ್ನು ಅರ್ಥಮಾಡಿಕೊಂಡು ಪ್ರಾಜೆಕ್ಟ್‌ಗಳನ್ನು ರಚಿಸಲು ಪ್ರಯತ್ನಿಸಿ; ಆದರೂ ಆ ಕೋಡ್ ಪ್ರತಿ ಪ್ರಾಜೆಕ್ಟ್-ಓರಿಯೆಂಟೆಡ್ ಪಾಠದ `/solution` ಫೋಲ್ಡರ್‌ಗಳಲ್ಲಿ ಲಭ್ಯವಿದೆ.
-- ಪೋಸ್ಟ್-ಉಪನ್ಯಾಸ ಕ್ವಿಜ್ ತೆಗೆದುಕೊಳ್ಳಿ.
-- ಚಾಲೆಂಜ್ ಅನ್ನು ಪೂರ್ಣಗೊಳಿಸಿ.
-- ಅಸೈನ್‌ಮೆಂಟ್ ಅನ್ನು ಪೂರ್ಣಗೊಳಿಸಿ.
-- ಒಂದು ಪಾಠ ಗುಂಪನ್ನು ಪೂರ್ಣಗೊಳಿಸುವ ನಂತರ, [ಚರ್ಚಾ ಫಲಕ](https://github.com/microsoft/ML-For-Beginners/discussions) ಗೆ ಭೇಟಿ ನೀಡಿ ಮತ್ತುಕ್ಕೆ ಯಥಾಸ್ಥಿತಿಯಲ್ಲಿ "learn out loud" ಮಾಡಿ (ಸರಿ PAT ರೂಬ್ರಿಕ್ ಅನ್ನು ಭರ್ತಿ ಮಾಡಿ). 'PAT' ಎಂಬುದು ಪ್ರಗತಿ ಮೌಲ್ಯಮಾಪನ ಸಾಧನ (Progress Assessment Tool) ಆಗಿದ್ದು, ನಿಮ್ಮ ಕಲಿಕೆಯನ್ನು ಮುಂದುವರಿಸಲು ನೀವು ಭರ್ತಿ ಮಾಡುವ ರೂಬ್ರಿಕ್ ಆಗಿದೆ. ನೀವು ಇತರ PATs ಗೆ ಪ್ರತಿಕ್ರಿಯೆ ನೀಡುವುದರಿಂದ ನಾವು ಜೊತೆಯಾಗಿ ಕಲಿಯಬಹುದು.
+- ಪೂರ್ವ-ಪಾಠ ಕ್ವಿಜ್ ಸಿದ್ದಮಾಡಿ.
+- ಉಪನ್ಯಾಸವನ್ನು ಓದಿ ಮತ್ತು ಚಟುವಟಿಕೆಗಳನ್ನು ಪೂರ್ಣಗೊಳಿಸಿ, ಪ್ರತಿ ನುಡಿದಾಟ ಪರೀಕ್ಷೆಯಲ್ಲಿ ವಿರಾಮಮೆಗೆ ಮತ್ತು ಪರಿಗಣನೆ ಮಾಡಿ.
+- ಪರಿಹಾರ ಕೋಡ್ ಅನ್ನು ನಡಿಸುವುದಕ್ಕಿಂತ ಪಾಠವನ್ನು ಅರ್ಥಮಾಡಿಕೊಂಡು ಪ್ರಾಜೆಕ್ಟುಗಳನ್ನು ರಚಿಸಲು ಯತ್ನಿಸಿ; ಆದರೂ ಆ ಕೋಡ್ ಪ್ರತಿ ಪ್ರಾಜೆಕ್ಟ್ ಗುರಿಯಾಗಿರುವ ಪಾಠದ `/solution` ಫೋಲ್ಡರ್ ಒಳಗೆ ಲಭ್ಯವಿದೆ.
+- ನಂತರದ ಪಾಠ ಕ್ವಿಜ್ ಹಾಕಿಕೊಳ್ಳಿ.
+- ಸವಾಲು ಪೂರ್ಣಗೊಳಿಸಿ.
+- ನಿಯೋಜನೆ ಪೂರ್ಣಗೊಳಿಸಿ.
+- ಪಾಠ ಗುಂಪನ್ನು ಮುಗಿಸಿದ ಬಳಿಕ, [ಚರ್ಚಾ ಫಲಕಕ್ಕೆ](https://github.com/microsoft/ML-For-Beginners/discussions) ಭೇಟಿ ನೀಡಿ ಮತ್ತು "ಹಾಸ್ಟು ತಿಳಿಸಿ" - ಸರಿಯಾದ PAT ರೂಬ್ರಿಕ್ ಅನ್ನು ಭರ್ತಿ ಮಾಡಿ. 'PAT' ಅಂದರೆ ಪ್ರಗತಿ ಮೌಲ್ಯಮಾಪನ ಉಪಕರಣ, ನಿಮ್ಮ ಅಧ್ಯಯನದ ಮುಂದುವರಿಗೆಯನ್ನು ಸಹಾಯಕವಾಗಿಸಲು ನೀವು ಭರ್ತಿ ಮಾಡುವ ರೂಬ್ರಿಕ್. ಇತರ PATಗಳಿಗೆ ಪ್ರತಿಕ್ರಿಯಿಸುವ ಮೂಲಕ ನಾವು ಒಟ್ಟಿಗೆ ಕಲಿಯಬಹುದು.
 
-> ಮುಂದಿನ ಅಧ್ಯಯನಕ್ಕಾಗಿ, ಈ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) ಮೋಡ್ಯೂಲ್‌ಗಳು ಮತ್ತು ಕಲಿಕಾ ಮಾರ್ಗಗಳನ್ನು ಅನುಸರಿಸುವುದನ್ನು ನಾವು ಶಿಫಾರಸು ಮಾಡುತ್ತೇವೆ.
+> ಹೆಚ್ಚಿನ ಅಧ್ಯಯನಕ್ಕಾಗಿ, ಈ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) ಮಾಯಾರುಗಳು ಮತ್ತು ಕಲಿಕೆಯ ಮಾರ್ಗಗಳು ಅನುಸರಿಸಲು ಶಿಫಾರಸು ಮಾಡುತ್ತೇವೆ.
 
-**ಶಿಕ್ಷಕರೇ**, ಈ ಪಠ್ಯಕ್ರಮವನ್ನು ಬಳಸುವ ಬಗ್ಗೆ ನಾವು [ಕೆಲವು ಸಲಹೆಗಳನ್ನು ಸೇರಿಸಿದ್ದೇವೆ](for-teachers.md).
+**ಶಿಕ್ಷಕರು**, ಈ ಪಠ್ಯಕ್ರಮವನ್ನು ಬಳಸುವುದಕ್ಕೆ ನಾವು ಕೆಲವು [ಸೂಚನೆಗಳನ್ನು ಸೇರಿಸಿರುವೆವು](for-teachers.md).
 
 ---
 
-## ವೀಡಿಯೊ ಪ್ರದರ್ಶನ
+## ವಿಡಿಯೋ ವಾಕ್ಥ್ರೋಸ್
 
-ಕೆಲವು ಪಾಠಗಳು ಚುಟುಕು ಆಕಾರದ ವೀಡಿಯೊಗಳಾಗಿ ಲಭ್ಯವಾಗಿವೆ. ನೀವು ಈ ಎಲ್ಲಾ ವೀಡಿಯೊಗಳನ್ನು ಪಾಠಗಳಲ್ಲಿ ಬಣ್ಣವಾಗಿ ಕಂಡುಹಿಡಿಯಬಹುದು, ಇಲ್ಲವೇ ಕೆಳಗಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ [Microsoft Developer YouTube ಚಾನೆಲಿನ 'ML for Beginners' ಪ್ಲೇಲಿಸ್ಟ್](https://aka.ms/ml-beginners-videos) ಭೈಗತಿಯನ್ನು ನೋಡಿ.
+ಕೆಲವು ಪಾಠಗಳು ಸಂಕ್ಷಿಪ್ತ ವೀಡಿಯೋ ರೂಪದಲ್ಲಿ ಲಭ್ಯವಿವೆ. ನೀವು ಈ ಎಲ್ಲವನ್ನೂ ಪಾಠಗಳಲ್ಲಿಯೇ ಅಥವಾ [Microsoft ಡೆವಲಪರ್ ಯೂಟ್ಯೂಬ್ ಚಾನೆಲ್‌ನ ML for Beginners ಪ್ಲೇಲಿಸ್ಟ್](https://aka.ms/ml-beginners-videos) ನಲ್ಲಿ ಕಂಡುಹಿಡಿಯಬಹುದು, ಕೆಳಗಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ.
 
-[![ML for Beginners ಬ್ಯಾನರ್](../../translated_images/kn/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/kn/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## ತಂಡದ ಪರಿಚಯ
+## ತಂಡವನ್ನು ಪರಿಚಯಿಸಿ
 
-[![ಪ್ರೋಮೋ ವಿಡಿಯೋ](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**ಗಿಫ್ ರಚಿಸಿದವರು** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**ಚಿತ್ರಣ** [ಮೊಹಿತ್ ಜೈಸಾಲ್](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 ಮೇಲಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ ಪ್ರಾಜೆಕ್ಟ್ ಮತ್ತು ಅದನ್ನು ರಚಿಸಿದ ಜನರ ಬಗ್ಗೆ ವಿಡಿಯೋ ನೋಡಿ!
+> 🎥 ಯೋಜನೆ ಮತ್ತು ಅದನ್ನು ರಚಿಸಿದವರ ಬಗ್ಗೆ ವೀಡಿಯೊಗಾಗಿ ಮೇಲಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ!
 
 ---
 
-## ಪಾಠವಿಧಿ
-
-ಈ ಪಾಠ್ಯಕ್ರಮವನ್ನು ನಿರ್ಮಿಸುವಾಗ ನಾವು ಎರಡು ಸಿದ್ಧಾಂತಗಳನ್ನು ಆರಿಸಿಕೊಂಡಿದ್ದೇವೆ: ಅದು ಕೈಯಲ್ಲಿ ಕೈಕೊಟ್ಟು ಮಾಡುವಂತೆ ಇರುವುದೇ "ಪ್ರಾಜೆಕ್ಟ್-ಆಧಾರಿತ" ಆಗಿರುವುದು ಮತ್ತು ಅದು "ಅವಧಿವಿಡಿ ಕ್ವಿಜ್‌ಗಳು" ಅನ್ನು gyakಳಿಸುವುದು. ಇದರ ಜೊತೆಗೆ, ಈ ಪಾಠ್ಯಕ್ರಮಕ್ಕೆ ಸಾಮಾನ್ಯ ಒಂದು **ಥೀಮ್** ಕಾಣಿಸಿಕೊಂಡಿದೆ ಅದು ಒತ್ತಡವನ್ನು ನೀಡುತ್ತದೆ.
-
-ವಿಷಯವು ಪ್ರಾಜೆಕ್ಟ್‌ಗಳಿಗೆ ಹೊಂದಿಕೆಯಾಗುವಂತೆ ಖಚಿತಪಡಿಸುವ ಮೂಲಕ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ಹೆಚ್ಚು ಆಕರ್ಷಕವಾಗುತ್ತದೆ ಹಾಗೂ ಕಲಿತ ವಿಷಯದ ನೆನಪು ಹೆಚ್ಚಾಗುತ್ತದೆ. ಜೊತೆಗೆ, ತರಗತಿಯ ಮುನ್ನ ಒಂದು ಕಡಿಮೆ-ಅವಶ್ಯಕತೆ ಇರುವ ಕ್ವಿಜ್ ವಿದ್ಯಾರ್ಥியின் ನಮ್ಮ ಒಂದು ವಿಷಯವನ್ನು ಕಲಿಯಲು ಉದ್ದೇಶವನ್ನು ಸರಿಗೊಳಿಸುತ್ತದೆ, ಮತ್ತು ತರಗತಿಯ ನಂತರದ ಎರಡನೇ ಕ್ವಿಜ್ ಹೆಚ್ಚುವರಿ ನೆನಪನ್ನು ಖಚಿತಪಡಿಸುತ್ತದೆ. ಈ ಪಠ್ಯಕ್ರಮವನ್ನು ಎಡ್ಜಸ್ಟ್ ಮಾಡುವಂತೆ ವಿನ್ಯಾಸಗೊಳಿಸಲಾಗಿದೆ ಮತ್ತು ಮೋಜುಗೊಳಿಸಿರುವುದು; ಇದನ್ನು ಸಂಪೂರ್ಣ ಅಥವಾ ಭಾಗಹವಾಗಿಯೂ ತೆಗೆದುಕೊಳ್ಳಬಹುದು. ಪ್ರಾಜೆಕ್ಟ್‌ಗಳು ಚಿಕ್ಕದಾಗಿ ಪ್ರಾರಂಭಿಸಿ 12-ವಾರಗಳ ಕೊನೆಯಲ್ಲಿ ಹೆಚ್ಚಾಗಿ ಸಂಕೀರ್ಣವಾಗುತ್ತವೆ. ಈ ಪಠ್ಯಕ್ರಮವು ಕೂಡ ML ನ ವಾಸ್ತವಿಕ ಅನ್ವಯಗಳ ಮೇಲೆ ಒಂದು ಸಹೋದ್ಯಕ್ಷೆ ಸೇರಿಸಿದೆ, ಇದನ್ನು ಹೆಚ್ಚುವರಿ ಕ್ರೆಡಿಟ್‌ಗೆ ಅಥವಾ ಚರ್ಚೆಯ ಆಧಾರದಾಗಿ ಬಳಸಬಹುದು.
-
-> ನಮುದಿನ [ಆಚರಣಾ ಸಂಹಿತೆ](CODE_OF_CONDUCT.md), [ಸಹಭಾಗತೆ](CONTRIBUTING.md), [ಅನುವಾದ](TRANSLATIONS.md), ಮತ್ತು [ತೊಂದರೆ ಪರಿಹಾರ ಮಾರ್ಗದರ್ಶಿ](TROUBLESHOOTING.md) ಮಾರ್ಗಸೂಚಿಗಳನ್ನು ನೋಡಿ. ನಿಮ್ಮ ರಚನಾತ್ಮಕ ಪ್ರತಿಕ್ರಿಯೆಯನ್ನು ನಾವು ಸ್ವಾಗತಿಸುತ್ತೇವೆ!
-
-## ಪ್ರತಿ ಪಾಠದಲ್ಲಿ ಒಳಗೊಂಡಿದೆ
-
-- ಆಯ್ಕೆಯಾದ ಸ್ಕೆಚ್‌ನೋಟ್
-- ಆಯ್ಕೆಯಾದ ಪೂರಕ ವೀಡಿಯೊ
-- ವೀಡಿಯೊ ವಾಕ್‌ಥ್ರೂ (ಕೆಲವು ಪಾಠಗಳಲ್ಲಿ ಮಾತ್ರ)
-- [ಪೂರ್ವ-ಉಪನ್ಯಾಸ ವಾರ್ಮ್-ಅಪ್ ಕ್ವಿಜ್](https://ff-quizzes.netlify.app/en/ml/)
-- ಲೇಖಿತ ಪಾಠ
-- ಪ್ರಾಜೆಕ್ಟ್-ಆಧಾರಿತ ಪಾಠಗಳಿಗೆ, ಪ್ರಾಜೆಕ್ಟ್ ಅನ್ನು ನಿರ್ಮಿಸುವದಕ್ಕೆ ಕ್ರಮಬದ್ಧ ಮಾರ್ಗದರ್ಶನಗಳು
-- ಜ್ಞಾನ ಪರಿಶೀಲನೆಗಳು
-- ಒಂದು ಚಾಲೆಂಜ್
-- ಪೂರಕ ಓದುವಿಕೆ
-- ಅಸೈನ್‌ಮೆಂಟ್
-- [ಪೋಸ್ಟ್-ಉಪನ್ಯಾಸ ಕ್ವಿಜ್](https://ff-quizzes.netlify.app/en/ml/)
-
-> **ಭಾಷೆಗಳ ಬಗ್ಗೆ ಒಂದು ಟಿಪ್**: ಈ ಪಾಠಗಳು_Primarily_ Python ನಲ್ಲಿ ರಚಿಸಲಾಗಿದೆ, ಆದರೆ ಅನೇಕ ಪಾಠಗಳು R ನಲ್ಲೂ ಲಭ್ಯವಿವೆ. R ಪಾಠವನ್ನು ಪೂರ್ಣಗೊಳಿಸಲು, `/solution` ಫೋಲ್ಡರ್‌ಗೆ ಹೋಗಿ R ಪಾಠಗಳನ್ನು ಹುಡುಕಿ. ಅವುಗಳಲ್ಲಿ `.rmd` ವಿಸ್ತರಣೆಯಿದೆ, ಇದು **R Markdown** ಫೈಲ್ ಅನ್ನು ಸೂಚಿಸುತ್ತದೆ. ಇದನ್ನು ಸರಳವಾಗಿ `code chunks` (R ಅಥವಾ ಇತರ ಭಾಷೆಗಳ) ಮತ್ತು `YAML header` (PDF ಮುಂತಾದ output ಗಳನ್ನು ಹೇಗೆ ರೂಪಿಸಲು ಮಾರ್ಗದರ್ಶನ ನೀಡುತ್ತದೆ) ಅನ್ನು `Markdown document` ನಲ್ಲಿ ಎಂಟ್ರಿ ಮಾಡುವುದಾಗಿ ವ್ಯಾಖ್ಯಾನಿಸಬಹುದು. ಹಾಗಿರುವುದರಿಂದ, ಇದು ಡೇಟಾ ಸೈನ್ಸ್‌ಗೆ ಉದಾಹರಣೆಯಾದ ರಚನಾತ್ಮಕ ಬರವಣಿಗೆ ಮಾದರಿಯನ್ನು ನೀಡುತ್ತದೆ ಏಕೆಂದರೆ ಇದು ನಿಮ್ಮ ಕೋಡ್, ಅದರ output ಮತ್ತು ನಿಮ್ಮ ಆಲೋಚನೆಗಳನ್ನು Markdown ನಲ್ಲಿ ಬರೆಯುವ ಮೂಲಕ ಸಂಯೋಜಿಸಲು ಅನುಮತಿಸುತ್ತದೆ. ಇತರ output ರೂಪಗಳಲ್ಲಿ PDF, HTML ಅಥವಾ Word ಗೆ R Markdown ಡಾಕ್ಯುಮೆಂಟುಗಳನ್ನು ರೆಂಡರ್ ಮಾಡಬಹುದು.
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-> **ಕ್ವಿಜ್ ಗಳ ಬಗ್ಗೆ ಟಿಪ್**: ಎಲ್ಲಾ ಕ್ವಿಜ್‌ಗಳು [quiz-app ಫೋಲ್ಡರ್](../../quiz-app) ನಲ್ಲಿ ಇರುವವು, ಒಟ್ಟು 52 ಕ್ವಿಜ್‌ಗಳು ಮತ್ತು ಪ್ರತಿ ಒಂದರಲ್ಲಿ ಮೂರು ಪ್ರಶ್ನೆಗಳು ಇವೆ. ಅವು ಪಾಠಗಳಲ್ಲಿ ಲಿಂಕ್ ಆಗಿವೆ ಆದರೆ ಕ್ವಿಜ್ ಆಪ್ ಅನ್ನು ಸ್ಥಳೀಯವಾಗಿ ಓಡಿಸಬಹುದಾಗಿದೆ; ಸ್ಥಳೀಯವಾಗಿ ಹೋಸ್ಟ್ ಮಾಡುವ ಅಥವಾ Azure ಗೆ ಡಿಪ್ಲಾಯ್ ಮಾಡಲು `quiz-app` ಫೋಲ್ಡರ್‌ನಲ್ಲಿನ ಸೂಚನೆಗಳನ್ನು ಅನುಸರಿಸಿ.
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-| ಪಾಠ ಸಂಖ್ಯೆ |                             ವಿಷಯ                              |                   ಪಾಠ ಗುಂಪು                   | ಅಧ್ಯಯನ ಉದ್ದೇಶಗಳು                                                                                                             |                                                              ಲಿಂಕ್ ಮಾಡಿದ ಪಾಠ                                                               |                        ಲೇಖಕ                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                ಯಂತ್ರ ಅಧ್ಯಯನಕ್ಕೆ ಪರಿಚಯ                |      [ಪರಿಚಯ](1-Introduction/README.md)       | ಯಂತ್ರ ಅಧ್ಯಯನದ ಮೂಲಭೂತ ತತ್ವಗಳನ್ನು ತಿಳಿಯಿರಿ                                                                                |                                             [ಪಾಠ](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                ಯಂತ್ರ ಅಧ್ಯಯನದ ಇತಿಹಾಸ                 |      [ಪರಿಚಯ](1-Introduction/README.md)       | ಈ ಕ್ಷೇತ್ರದ ಮೂಲಭೂತ ಇತಿಹಾಸವನ್ನು ತಿಳಿಯಿರಿ                                                                                         |                                            [ಪಾಠ](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 ನ್ಯಾಯತೆ ಮತ್ತು ಯಂತ್ರ ಅಧ್ಯಯನ                  |      [ಪರಿಚಯ](1-Introduction/README.md)       | ಯಂತ್ರ ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸುವಾಗ ಮತ್ತು ಬಳಸುವಾಗ ವಿದ್ಯಾರ್ಥಿಗಳು ನ್ಯಾಯತೆಯನ್ನು ಕುರಿತು ಪರಿಗಣಿಸಬೇಕಾದ ಪ್ರಮುಖ ತತ್ತ್ವಶಾಸ್ತ್ರೀಯ ವಿಷಯಗಳು ಯಾವುವು? |                                              [ಪಾಠ](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                ಯಂತ್ರ ಅಧ್ಯಯನ ತಂತ್ರಗಳು                 |      [ಪರಿಚಯ](1-Introduction/README.md)       | ಯಂತ್ರ ಅಧ್ಯಯನ ಸಂಶೋಧಕರು ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸಲು ಯಾವ ತಂತ್ರಗಳನ್ನು ಬಳಸುತ್ತಾರೆ?                                                                       |                                          [ಪಾಠ](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   ರಿಗ್ರೆಶನ್ ಗೆ ಪರಿಚಯ                   |        [Regression](2-Regression/README.md)         | Python ಮತ್ತು Scikit-learn ಬಳಸಿ ರಿಗ್ರೆಶನ್ ಮಾದರಿಗಳೊಂದಿಗೆ ಪ್ರಾರಂಭಿಸಿ                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                ಉತ್ತರ ಅಮೇರಿಕಾದ ಕುಂಭಳಕಾಯಿ ಬೆಲೆಗಳು 🎃                |        [Regression](2-Regression/README.md)         | ಯಂತ್ರ ಅಧ್ಯಯನಕ್ಕಾಗಿ ತಯಾರಿಯಲ್ಲಿ ಡೇಟಾವನ್ನು ದೃಶ್ಯೀಕರಿಸಿ ಮತ್ತು ಶುದ್ಧಗೊಳಿಸಿ                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                ಉತ್ತರ ಅಮೇರಿಕಾದ ಕುಂಭಳಕಾಯಿ ಬೆಲೆಗಳು 🎃                |        [Regression](2-Regression/README.md)         | ಲೇಖೀಯ ಮತ್ತು ಬಹುಪದ ರಿಗ್ರೆಶನ್ ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸಿ                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                ಉತ್ತರ ಅಮೇರಿಕಾದ ಕುಂಭಳಕಾಯಿ ಬೆಲೆಗಳು 🎃                |        [Regression](2-Regression/README.md)         | ಲಾಜಿಸ್ಟಿಕ್ ರಿಗ್ರೆಶನ್ ಮಾದರಿಯನ್ನು ನಿರ್ಮಿಸಿ                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          ವೆಬ್ ಆಪ್ 🔌                          |           [Web App](3-Web-App/README.md)            | ತರಬೇತಿ ಪಡೆಯಲಾದ ನಿಮ್ಮ ಮಾದರಿಯನ್ನು ಬಳಸಲು ಒಂದು ವೆಬ್ ಆಪ್ ನಿರ್ಮಿಸಿ                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 ವರ್ಗೀಕರಣಕ್ಕೆ ಪರಿಚಯ                 |    [Classification](4-Classification/README.md)     | ಡೇಟಾವನ್ನು ಶುದ್ಧಗೊಳಿಸಿ, ಸಿದ್ಧಪಡಿಸಿ ಮತ್ತು ದೃಶ್ಯೀಕರಿಸಿ; ವರ್ಗೀಕರಣಕ್ಕೆ ಪರಿಚಯ                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಅಡುಗೆಗಳು 🍜             |    [Classification](4-Classification/README.md)     | ವರ್ಗೀಕರಣಗಳ ಪರಿಚಯ                                                                                                     | [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       |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಅಡುಗೆಗಳು 🍜             |    [Classification](4-Classification/README.md)     | ಇನ್ನಷ್ಟು ವರ್ಗೀಕರಣಗಳು                                                                                                                | [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       |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಅಡುಗೆಗಳು 🍜             |    [Classification](4-Classification/README.md)     | ನಿಮ್ಮ ಮಾದರಿಯನ್ನು ಉಪಯೋಗಿಸಿ ಒಂದು ಶಿಫಾರಸು ಮಾಡುವ ವೆಬ್ ಆಪ್ ನಿರ್ಮಿಸಿ                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   ಕ್ಲಸ್ಟರಿಂಗ್ ಗೆ ಪರಿಚಯ                   |        [Clustering](5-Clustering/README.md)         | ಡೇಟಾವನ್ನು ಶುದ್ಧಗೊಳಿಸಿ, ಸಿದ್ಧಪಡಿಸಿ ಮತ್ತು ದೃಶ್ಯೀಕರಿಸಿ; ಕ್ಲಸ್ಟರಿಂಗ್ ಗೆ ಪರಿಚಯ                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              ನೈಜೀರಿಯನ್ ಸಂಗೀತ ರುಚಿಗಳನ್ನು ಅನ್ವೇಷಣೆ 🎧              |        [Clustering](5-Clustering/README.md)         | K-Means ಕ್ಲಸ್ಟರಿಂಗ್ ವಿಧಾನವನ್ನು ಅನ್ವೇಷಿಸಿ                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        ಸ್ವಾಭಾವಿಕ ಭಾಷೆ ಸಂಸ್ಕರಣಕ್ಕೆ ಪರಿಚಯ ☕️         |   [Natural language processing](6-NLP/README.md)    | ಸರಳ ಬಾಟ್ ಅನ್ನು ನಿರ್ಮಿಸುವ ಮೂಲಕ NLP ಬಗ್ಗೆ ಮೂಲಭೂತಗಳನ್ನು ಕಲಿಯಿರಿ                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      ಸಾಮಾನ್ಯ NLP ಕಾರ್ಯಗಳು ☕️                      |   [Natural language processing](6-NLP/README.md)    | ಭಾಷಾ ರಚನೆಗಳೊಂದಿಗೆ ವ್ಯವಹರಿಸುವಾಗ ಬೇಕಾದ ಸಾಮಾನ್ಯ ಕಾರ್ಯಗಳನ್ನು ಅರ್ಥಮಾಡಿಕೊಂಡು ನಿಮ್ಮ NLP ಜ್ಞಾನವನ್ನು ಗಾಢಗೊಳಿಸಿಕೊಳ್ಳಿ                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             ಭಾಷಾಂತರ ಮತ್ತು ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ ♥️              |   [Natural language processing](6-NLP/README.md)    | Jane Austen ಅವರೊಂದಿಗೆ ಭಾಷಾಂತರ ಮತ್ತು ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  ಯುರೋಪಿನ ರೋಮ್ಯಾಂಟಿಕ್ ಹೋಟೆಲ್ಗಳು ♥️                  |   [Natural language processing](6-NLP/README.md)    | ಹೋಟೆಲ್ ವಿಮರ್ಶೆಗಳೊಂದಿಗೆ ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  ಯುರೋಪಿನ ರೋಮ್ಯಾಂಟಿಕ್ ಹೋಟೆಲ್ಗಳು ♥️                  |   [Natural language processing](6-NLP/README.md)    | ಹೋಟೆಲ್ ವಿಮರ್ಶೆಗಳೊಂದಿಗೆ ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            ಕಾಲಾನುಗತ ಸರಣಿ ಭವಿಷ್ಯವಾಣಿಗೆ ಪರಿಚಯ             |        [Time series](7-TimeSeries/README.md)        | ಕಾಲಾನುಗತ ಸರಣಿಗಳ ಭವಿಷ್ಯವಾಣಿ ಪರಿಚಯ                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ World Power Usage ⚡️ - ARIMAನೊಂದಿಗೆ ಸಮಯ ಸರಣಿ ಭವಿಷ್ಯವಾಣಿ |        [Time series](7-TimeSeries/README.md)        | ARIMA ಬಳಸಿ ಸಮಯ ಸರಣಿ ಭವಿಷ್ಯವಾಣಿ                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ World Power Usage ⚡️ - SVR ನೊಂದಿಗೆ ಸಮಯ ಸರಣಿ ಭವಿಷ್ಯವಾಣಿ  |        [Time series](7-TimeSeries/README.md)        | Support Vector Regressor ಬಳಸಿ ಸಮಯ ಸರಣಿ ಭವಿಷ್ಯವಾಣಿ                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             ರೀಇನ್ಫೋರ್ಸ್ಮೆಂಟ್ ಲರ್ನಿಂಗ್ ಗೆ ಪರಿಚಯ             | [Reinforcement learning](8-Reinforcement/README.md) | Q-Learning ಬಳಸಿ ರೀಇನ್ಫೋರ್ಸ್ಮೆಂಟ್ ಲರ್ನಿಂಗ್ ಗೆ ಪರಿಚಯ                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 ಪೀಟರ್ ಅನ್ನು ನರಿ ತಪ್ಪಿಸಲು ಸಹಾಯಿಸಿ! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | ರೀಇನ್ಫೋರ್ಸ್ಮೆಂಟ್ ಲರ್ನಿಂಗ್ Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            ವಾಸ್ತವಿಕ ಜಗತ್ತಿನ ML ಸನ್ನಿವೇಶಗಳು ಮತ್ತು ಅನ್ವಯಿಕೆಗಳು            |      [ML in the Wild](9-Real-World/README.md)       | ಸಾಂಪ್ರದಾಯಿಕ ML ನ ರೋಚಕ ಮತ್ತು ಬಹಿರಂಗವಾಗಿಸುವ ವಾಸ್ತವಿಕ ಜಗತ್ತಿನ ಅನ್ವಯಿಕೆಗಳು                                                               |                                             [ಪಾಠ](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            RAI ಡ್ಯಾಶ್‌ಬೋರ್ಡ್ ಬಳಸಿ ML ನಲ್ಲಿ ಮಾದರಿ ಡಿಬಗಿಂಗ್          |      [ML in the Wild](9-Real-World/README.md)       | RAI ಡ್ಯಾಶ್‌ಬೋರ್ಡ್ ಘಟಕಗಳನ್ನು ಬಳಸಿ ಯಂತ್ರ ಅಧ್ಯಯನದಲ್ಲಿ ಮಾದರಿ ಡಿಬಗಿಂಗ್                                                              |                                             [ಪಾಠ](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
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-> [ಈ ಕೋರ್ಸಿನ ಎಲ್ಲಾ ಹೆಚ್ಚುವರಿ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಮ್ಮ Microsoft Learn ಸಂಗ್ರಹದಲ್ಲಿ ಕಂಡುಹಿಡಿಯಿರಿ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+## ಶಿಕ್ಷಣಶಾಸ್ತ್ರ
+
+ನಾವು ಈ ಪಠ್ಯಕ್ರಮವನ್ನು ರಚಿಸುವಾಗ ಎರಡು ಶಿಕ್ಷಣ ತತ್ವಗಳನ್ನು ಆಯ್ಕೆ ಮಾಡಿದ್ದೇವೆ: ಇದು ಕೈಮೇಲೆ **ಯೋಜನೆಾಧಾರಿತ** ಆಗಿರಬೇಕು ಮತ್ತು ಅದರಲ್ಲಿ **ಅನೇಕ ಕ್ವಿಜ್‌ಗಳು** ಇರಬೇಕು. ಇದಲ್ಲದೆ, ಈ ಪಠ್ಯಕ್ರಮದಲ್ಲಿ ಸಾಮಾನ್ಯ **ಥೀಮ್ಅಂಶ** ಕೂಡ ಇದೆ, ಇದರಿಂದ ಅದು ಸಮಗ್ರತೆಯನ್ನು ಪಡೆಯುತ್ತದೆ.
+
+ವಿಷಯಗಳು ಯೋಜನೆಗಳಿಗೆ ಹೊಂದಿಕೊಂಡಿರುವುದರಿಂದ, ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ವ್ಯವಹಾರಿಕವಾಗುತ್ತದೆ ಮತ್ತು ಕಲಿತ ವಿಚಾರಗಳ ನಂಚಿಕೆ ಹೆಚ್ಚಿನ ಮಟ್ಟಕ್ಕೆ ತಿರುಗಿಬರುತ್ತದೆ. ಇದಲ್ಲದೆ, ತರಗತಿ ಮುಂಚೆ ಕಡಿಮೆ ಬಲದ ಕ್ವಿಜ್ ಒಂದು ವಿಷಯ ಕಲಿಯಲು ವಿದ್ಯಾರ್ಥಿಯ ಉದ್ದೇಶವನ್ನು ಆಗ<|vq_image_14350|><|vq_image_13388|><|vq_image_13388|><|vq_image_13388|><|vq_image_13388|><|vq_image_3256|><|vq_image_9434|><|vq_image_13388|><|vq_image_8244|><|vq_image_12999|><|vq_image_14098|><|vq_image_7681|><|vq_image_13388|><|vq_image_13389|><|image_border_14|><|vq_image_14099|><|vq_image_5276|><|vq_image_11020|><|vq_image_1881|><|vq_image_13388|><|vq_image_2301|><|vq_image_15595|><|vq_image_15595|><|vq_image_11755|><|vq_image_14336|><|vq_image_7681|><|vq_image_15510|><|vq_image_2699|><|vq_image_1881|><|vq_image_5132|><|vq_image_14099|><|image_border_15|><|vq_image_14850|><|vq_image_8244|><|vq_image_2641|><|vq_image_2699|><|vq_image_11118|><|vq_image_15595|><|vq_image_15595|><|vq_image_13748|><|vq_image_11118|><|vq_image_12895|><|vq_image_12895|><|vq_image_2699|><|vq_image_8244|><|vq_image_8244|><|vq_image_5276|><|vq_image_14099|>
+> **ಕ್ವಿಜ್ಗಳ ಬಗ್ಗೆ ಒಂದು ಟಿಪ್ಪಣಿ**: ಎಲ್ಲಾ ಕ್ವಿಜ್ಗಳು [Quiz App folder](../../quiz-app) ನಲ್ಲಿ ಇದ್ದು, ಒಟ್ಟು 52 ಕ್ವಿಜ್‌ಗಳಿದ್ದು ಪ್ರತಿಯೊಂದರಲ್ಲೂ ಮೂರು ಪ್ರಶ್ನೆಗಳಿವೆ. ಅವುಗಳು ಪಾಠಗಳೊಳಗಿಂದ ಲಿಂಕ್ ಮಾಡಲ್ಪಟ್ಟಿದ್ದರೂ, ಕ್ವಿಜ್ ಆಪ್ ಅನ್ನು ಸ್ಥಳೀಯವಾಗಿಯೂ ಚಾಲನೆ ಮಾಡಬಹುದು; ಸ್ಥಳೀಯವಾಗಿ ಹೋಸ್ಟ್ ಅಥವಾ Azure ಗೆ ನಿಯೋಜಿಸಲು `quiz-app` ಫೋಲ್ಡರ್‌ನ ನಿರ್ದೇಶನವನ್ನು ಅನುಸರಿಸಿ.
+
+| ಪಾಠ ಸಂಖ್ಯೆ |                             ವಿಷಯ                              |                   ಪಾಠ ಗುಂಪು                   | ಕಲಿಕಾ ಉದ್ದೇಶಗಳು                                                                                                             |                                                              ಲಿಂಕ್ ಮಾಡಿದ ಪಾಠ                                                               |                        ಲೇಖಕ                        |
+| :-------: | :-----------------------------------------------------------: | :---------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
+|    01     |                ಯಂತ್ರ ಅಧ್ಯಯನಕ್ಕೆ ಪರಿಚಯ                       |      [Introduction](1-Introduction/README.md)       | ಯಂತ್ರ ಅಧ್ಯಯನದ ಹಿನ್ನಲೆಯಲ್ಲಿ ಇರುವ ಮೂಲಭೂತ ತತ್ವಗಳನ್ನು ಕಲಿಯಿರಿ                                                                          |                                             [ಪಾಠ](1-Introduction/1-intro-to-ML/README.md)                                             |                       ಮುಖಮ್ಮದ್                       |
+|    02     |                ಯಂತ್ರ ಅಧ್ಯಯನದ ಇತಿಹಾಸ                        |      [Introduction](1-Introduction/README.md)       | ಈ ಕ್ಷೇತ್ರದ ಇತಿಹಾಸವನ್ನು ತಿಳಿಯಿರಿ                                                                                                   |                                            [ಪಾಠ](1-Introduction/2-history-of-ML/README.md)                                            |                     ಜೆನ್ ಮತ್ತು ಎಮಿ                      |
+|    03     |                 ನ್ಯಾಯತೀರ್ಮಾನ ಮತ್ತು ಯಂತ್ರ ಅಧ್ಯಯನ               |      [Introduction](1-Introduction/README.md)       | ಯುದ್ಧಬದ್ಧ ಯಂತ್ರ ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸುವಾಗ ಮತ್ತು ಅನ್ವಯಿಸುವಾಗ ವಿದ್ಯಾರ್ಥಿಗಳು ಪರಿಗಣಿಸಬೇಕಾದ ನ್ಯಾಯತೀರ್ಮಾನಕ್ಕೆ ಸಂಬಂಧಿಸಿದ ಪ್ರಮುಖ ತತ್ವಶಾಸ್ತ್ರೀಯ ವಿಷಯಗಳು ಏನು? |                                              [ಪಾಠ](1-Introduction/3-fairness/README.md)                                               |                        ತೊಮೊಮಿ                        |
+|    04     |                ಯಂತ್ರ ಅಧ್ಯಯನ ತಂತ್ರಗಳು                         |      [Introduction](1-Introduction/README.md)       | ಯಂತ್ರ ಅಧ್ಯಯನ ಗವೇಶಕರು ಯಂತ್ರ ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸಲು ಬಳಸುವ ತಂತ್ರಗಳು ಯಾವವು?                                                                       |                                          [ಪಾಠ](1-Introduction/4-techniques-of-ML/README.md)                                           |                    ಕ್ರಿಸ್ ಮತ್ತು ಜೆನ್                     |
+|    05     |                   ರಿಗ್ರೇಶನ್ ಗೆ ಪರಿಚಯ                       |        [Regression](2-Regression/README.md)         | ರಿಗ್ರೇಶನ್ ಮಾದರಿಗಳಿಗೆ ಪೈಥಾನ್ ಮತ್ತು ಸ್ಕೈಕಿಟ್-ಲರ್ನ್ ಬಳಸಿ ಪ್ರಾರಂಭಿಸಿ                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      ಜೆನ್ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    06     |                ಉತ್ತರ ಅಮೆರಿಕನ್ ಗೋಡಂಬಿ ಬೆಲೆಗಳು 🎃             |        [Regression](2-Regression/README.md)         | ಯಂತ್ರ ಅಧ್ಯಯನದ ಪೂರ್ವತಯಾರಿಗಾಗಿ ಡೇಟಾವನ್ನು ದೃಶ್ಯೀಕರಿಸಿ ಮತ್ತು ಶುದ್ಧಗೊಳಿಸಿ                                                                              |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      ಜೆನ್ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    07     |                ಉತ್ತರ ಅಮೆರಿಕನ್ ಗೋಡಂಬಿ ಬೆಲೆಗಳು 🎃             |        [Regression](2-Regression/README.md)         | ರೇಖೀಯ ಮತ್ತು ಬಹುಪದ ರಿಗ್ರೇಶನ್ ಮಾದರಿಗಳನ್ನು ನಿರ್ಮಿಸಿ                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      ಜೆನ್ ಮತ್ತು ಡ್ಮಿತ್ರಿ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    08     |                ಉತ್ತರ ಅಮೆರಿಕನ್ ಗೋಡಂಬಿ ಬೆಲೆಗಳು 🎃             |        [Regression](2-Regression/README.md)         | ಲಾಜಿಸ್ಟಿಕ್ ರಿಗ್ರೇಶನ್ ಮಾದರಿಯನ್ನು ನಿರ್ಮಿಸಿ                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      ಜೆನ್ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    09     |                          ವೆಬ್ ಅಪ್ಲಿಕೇಶನ್ 🔌                 |           [Web App](3-Web-App/README.md)            | ನಿಮ್ಮ ತರಬೇತುಗೊಂಡ ಮಾದರಿಯನ್ನು ಬಳಸಲು ವೆಬ್ ಅಪ್ಲಿಕೇಶನ್ ನಿರ್ಮಿಸಿ                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         ಜೆನ್                          |
+|    10     |                 ವರ್ಗೀಕರಣಕ್ಕೆ ಪರಿಚಯ                        |    [Classification](4-Classification/README.md)     | ನಿಮ್ಮ ಡೇಟಾವನ್ನು ಶುದ್ಧಗೊಳಿಸಿ, ಪೂರ್ವತಯಾರು ಮಾಡಿ ಮತ್ತು ದೃಶ್ಯೀಕರಿಸಿ; ವರ್ಗೀಕರಣಕ್ಕೆ ಪರಿಚಯ                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | ಜೆನ್ ಮತ್ತು ಕ್ಯಾಸಿ • ಎರಿಕ್ ವಾಂಜೌ |
+|    11     |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಆಹಾರಗಳು 🍜           |    [Classification](4-Classification/README.md)     | ವರ್ಗೀಕರಣಕಾರರಿಗೆ ಪರಿಚಯ                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | ಜೆನ್ ಮತ್ತು ಕ್ಯಾಸಿ • ಎರಿಕ್ ವಾಂಜೌ |
+|    12     |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಆಹಾರಗಳು 🍜           |    [Classification](4-Classification/README.md)     | ಇನ್ನಷ್ಟು ವರ್ಗೀಕರಣಕಾರರು                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | ಜೆನ್ ಮತ್ತು ಕ್ಯಾಸಿ • ಎರಿಕ್ ವಾಂಜೌ |
+|    13     |             ರುಚಿಕರ ಏಷ್ಯನ್ ಮತ್ತು ಭಾರತೀಯ ಆಹಾರಗಳು 🍜           |    [Classification](4-Classification/README.md)     | ನಿಮ್ಮ ಮಾದರಿಯನ್ನು ಬಳಸಿ ಶಿಫಾರಸು ಮಾಡುವ ವೆಬ್ ಅಪ್ಲಿಕೇಶನ್ ನಿರ್ಮಿಸಿ                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         ಜೆನ್                          |
+|    14     |                   ಕ್ಲಸ್ಟರಿಂಗ್ ಗೆ ಪರಿಚಯ                        |        [Clustering](5-Clustering/README.md)         | ನಿಮ್ಮ ಡೇಟಾವನ್ನು ಶುದ್ಧಗೊಳಿಸಿ, ಪೂರ್ವತಯಾರು ಮಾಡಿ ಮತ್ತು ದೃಶ್ಯೀಕರಿಸಿ; ಕ್ಲಸ್ಟರಿಂಗ್ ಗೆ ಪರಿಚಯ                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      ಜೆನ್ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    15     |              ನೈಜೀರಿಯನ್ ಸಂಗೀತ ರುಚಿಗಳನ್ನು ಅನ್ವೇಷಣೆ 🎧           |        [Clustering](5-Clustering/README.md)         | K-ಮೀನ್ಸ್ ಕ್ಲಸ್ಟರಿಂಗ್ ವಿಧಾನವನ್ನು ಅನ್ವೇಷಿಸಿ                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      ಜೆನ್ • ಎರಿಕ್ ವಾಂಜೌ       |
+|    16     |        ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆಗೆ ಪರಿಚಯ ☕️                  |   [Natural language processing](6-NLP/README.md)    | ಸರಳ ಬಾಟ್ ನಿರ್ಮಿಸುವ ಮೂಲಕ NLP ನ ಮೂಲಭೂತಗಳನ್ನು ಕಲಿಯಿರಿ                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       ಸ್ಟೀಫನ್                        |
+|    17     |                      ಸಾಮಾನ್ಯ NLP ಕಾರ್ಯಗಳು ☕️                |   [Natural language processing](6-NLP/README.md)    | ಭಾಷೆಯ ರಚನೆಗಳೊಂದಿಗೆ ವ್ಯವಹರಿಸುವಾಗ ಬೇಕಾದ ಸಾಮಾನ್ಯ ಕಾರ್ಯಗಳನ್ನು ಸಮರ್ಥವಾಗಿ ತಿಳಿದುಕೊಳ್ಳಿ                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       ಸ್ಟೀಫನ್                        |
+|    18     |             ಭಾಷಾಂತರ ಮತ್ತು ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ ♥️           |   [Natural language processing](6-NLP/README.md)    | ಜೆನ್ ಅಸ್ಟನ್ ಬಳಸಿ ಭಾಷಾಂತರ ಮತ್ತು ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       ಸ್ಟೀಫನ್                        |
+|    19     |                  ಯುರೋಪಿನ ರೋಮ್ಯಾಂಟಿಕ್ ಹೋಟೆಲ್ಗಳು ♥️          |   [Natural language processing](6-NLP/README.md)    | ಹೋಟೆಲ್ ವಿಮರ್ಶೆಗಳೊಂದಿಗೆ ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       ಸ್ಟೀಫನ್                        |
+|    20     |                  ಯುರೋಪಿನ ರೋಮ್ಯಾಂಟಿಕ್ ಹೋಟೆಲ್ಗಳು ♥️          |   [Natural language processing](6-NLP/README.md)    | ಹೋಟೆಲ್ ವಿಮರ್ಶೆಗಳೊಂದಿಗೆ ಭಾವನಾತ್ಮಕ ವಿಶ್ಲೇಷಣೆ 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       ಸ್ಟೀಫನ್                        |
+|    21     |            ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆಗೆ ಪರಿಚಯ                     |        [Time series](7-TimeSeries/README.md)        | ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆಗೆ ಪರಿಚಯ                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      ಫ್ರಾನ್ಸೆಸ್ಕಾ                       |
+|    22     | ⚡️ ವಿಶ್ವ ವಿದ್ಯುತ್ ಬಳಕೆ ⚡️ - ARIMA ಯೊಂದಿಗೆ ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆ  |        [Time series](7-TimeSeries/README.md)        | ARIMA ಯೊಂದಿಗೆ ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆ                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      ಫ್ರಾನ್ಸೆಸ್ಕಾ                       |
+|    23     |  ⚡️ ವಿಶ್ವ ವಿದ್ಯುತ್ ಬಳಕೆ ⚡️ - SVR ಯೊಂದಿಗೆ ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆ  |        [Time series](7-TimeSeries/README.md)        | ಸಪೋರ್ಟ್ ವೆಕ್ಟರ್ ರಿಗ್ರೇಶರ್ ಬಳಸಿ ಕಾಲ ಧಾರಾ ಮುಂಭಾವನೆ                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       ಅನಿರ್ಬಾನ್                        |
+|    24     |             ಬಲವರ್ಧನೆ ಕಲಿಕೆಗೆ ಪರಿಚಯ                          | [Reinforcement learning](8-Reinforcement/README.md) | Q-ಲರ್ನಿಂಗ್ ಬಳಸಿ ಬಲವರ್ಧನೆ ಕಲಿಕೆಗೆ ಪರಿಚಯ                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        ಡ್ಮಿತ್ರಿ                        |
+|    25     |                 ಪೀಟರ್ ನಾಡಿ ಆನೆಯನ್ನು ತಪ್ಪಿಸಿ! 🐺               | [Reinforcement learning](8-Reinforcement/README.md) | ರೀನ್ಫೋರ್ಸ್‌ಮೆಂಟ್ ಲರ್ನಿಂಗ್ ಜಿಮ್                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        ಡ್ಮಿತ್ರಿ                        |
+|  ಪೋಷ್‌ಸ್ಕ್ರಿಪ್ಟ್  |            ವಾಸ್ತವಿಕ ಜಗತ್ತಿನ ಯಂತ್ರ ಅಧ್ಯಯನ ದೃಶ್ಯಗಳು ಮತ್ತು ಅನ್ವಯಣೆಗಳು            |      [ML in the Wild](9-Real-World/README.md)       | ಕ್ಲಾಸಿಕಲ್ ಎಂಎಲ್‌ನ ರೋಚಕ ಮತ್ತು ಬಹಿರಂಗ ಪ್ರಕಾರಗಳು                                                               |                                             [ಪಾಠ](9-Real-World/1-Applications/README.md)                                              |                         ತಂಡ                         |
+|  ಪೋಷ್‌ಸ್ಕ್ರಿಪ್ಟ್  |            RAI ಡ್ಯಾಶ್ಬೋರ್ಡ್ ಬಳಸಿ ಎಂಎಲ್ನಲ್ಲಿನ ಮಾದರಿ ಡಿಬಗ್ಗಿಂಗ್          |      [ML in the Wild](9-Real-World/README.md)       | ರೆಸ್ಪಾನ್ಸಿಬಲ್ AI ಡ್ಯಾಶ್ಬೋರ್ಡ್ ಘಟಕಗಳನ್ನು ಬಳಸಿ ಯಂತ್ರ ಅಧ್ಯಯನದಲ್ಲಿ ಮಾದರಿ ಡಿಬಗ್ಗಿಂಗ್                                                              |                                             [ಪಾಠ](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         ರೂತ್ ಯಾಕುಬು                       |
+
+> [ಈ ಕೋರ್ಸ್‌ಗೆ ಸಂಬಂಧಿಸಿದ ಎಲ್ಲ ಹೆಚ್ಚುವರಿ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಮ್ಮ Microsoft Learn ಸಂಗ್ರಹದಲ್ಲಿ ಕಂಡುಕೊಳ್ಳಿ](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## ಆಫ್‌ಲೈನ್ ಪ್ರವೇಶ
 
-ನೀವು [Docsify](https://docsify.js.org/#/) ಬಳಸಿಕೊಂಡು ಈ ಡಾಕ್ಯುಮೆಂಟೇಶನನ್ನು ಆಫ್‌ಲೈನ್‌ನಲ್ಲಿ ಚಾಲನೆ ಮಾಡಬಹುದು. ಈ ರೆಪೋವನ್ನು ಫೋರ್ಕ್ ಮಾಡಿ, ನಿಮ್ಮ ಸ್ಥಳೀಯ ಯಂತ್ರದಲ್ಲಿ [Docsify ಅನ್ನು ಇನ್‌ಸ್ಟಾಲ್ ಮಾಡಿ](https://docsify.js.org/#/quickstart), ಮತ್ತು ನಂತರ ಈ ರೆಪೋನ ರೂಟ್ ಫೋಲ್ಡರ್‌ನಲ್ಲಿ `docsify serve` ಎಂದು ಟೈಪ್ ಮಾಡಿ. ವೆಬ್‌ಸೈಟ್ ನಿಮ್ಮ ಲೋಕಲ್‌ಹೋಸ್ಟ್‌ನಲ್ಲಿ ಪೋರ್ಟ್ 3000 ಮೇಲೆ ಸರ್ವ್ ಆಗುತ್ತದೆ: `localhost:3000`.
+[Docsify](https://docsify.js.org/#/) ಬಳಸಿ ನೀವು ಈ ದಾಖಲೆಗಳನ್ನು ಆಫ್‌ಲೈನ್‌ನಲ್ಲಿ ಚಾಲನೆ ಮಾಡಬಹುದು. ಈ ರೆಪೊವನ್ನು ಫೋರ್ಕ್ ಮಾಡಿ, ನಿಮ್ಮ ಸ್ಥಳೀಯ ಯಂತ್ರದಲ್ಲಿ [Docsify ಅನ್ನು ಸ್ಥಾಪಿಸಿ](https://docsify.js.org/#/quickstart), ನಂತರ ಈ ರೆಪೊ ರೂಟ್ ಫೋಲ್ಡರ್‌ನಲ್ಲಿ `docsify serve` ಟೈಪ್ ಮಾಡಿ. ವೆಬ್‌സೈಟ್ ನಿಮ್ಮ ಲೊಕಲ್‌ಹೋಸ್ಟ್‌ನಲ್ಲಿ ಪೋರ್ಟ್ 3000 ರಲ್ಲಿ ಲಭ್ಯವಾಗುತ್ತದೆ: `localhost:3000`.
 
 ## PDF ಗಳು
 
-ಲಿಂಕುಗಳೊಂದಿಗೆ ಪಠ್ಯಕ್ರಮದ PDF ಅನ್ನು [ಇಲ್ಲಿ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) ಕಂಡುಹಿಡಿಯಿರಿ。
+[ಇಲ್ಲಿ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) ಲಿಂಕ್‌ಗಳೊಂದಿಗೆ ಪಠ್ಯಕ್ರಮದ pdf ಅನ್ನು ಕಂಡುಹಿಡಿಯಿರಿ.
 
 
-## 🎒 ಇತರೆ ಕೋರ್ಸ್‌ಗಳು 
+## 🎒 ಇತರ ಕೋರ್ಸುಗಳು 
 
-ನಮ್ಮ ತಂಡ ಇತರ ಕೋರ್ಸ್‌ಗಳನ್ನು ರಚಿಸುತ್ತದೆ! ಪರಿಶೀಲಿಸಿ:
+ನಮ್ಮ ತಂಡ ಇತರ ಕೋರ್ಸುಗಳನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ! ನೋಡಿ:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j ಆರಂಭಿಕರಿಗಾಗಿ](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 ಆರಂಭಿಕರಿಗಾಗಿ](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 ಆರಂಭಿಕರಿಗಾಗಿ](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 ಆರಂಭಿಕರಿಗಾಗಿ](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 ಆರಂಭಿಕರಿಗಾಗಿ](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 ಆರಂಭಿಕರಿಗಾಗಿ](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)
 
 ---
  
-### ಜನರೇಟಿವ್ AI ಸರಣಿ
-[![ಜನರೇಟಿವ್ AI ಆರಂಭಿಕರಿಗಾಗಿ](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)
-[![ಜನರೇಟಿವ್ 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)
-[![ಜನರೇಟಿವ್ 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)
-[![ಜನರೇಟಿವ್ 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 Series
+[![ಹೊಸವರಿಗಾಗಿ ಜನರೆಟಿವ್ 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)
+[![ಜನರೆಟಿವ್ 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)
+[![ಜನರೆಟಿವ್ AI (ಜಾವಾ)](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)
+[![ಜನರೆಟಿವ್ AI (ಜಾವಾಸ್ಕ್ರಿಪ್ಟ್)](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)
 
 ---
  
-### ಮೂಲಭೂತ ಕಲಿಕೆ
-[![ಆರಂಭಿಕರಿಗಾಗಿ ML](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)
-[![ಆರಂಭಿಕರಿಗಾಗಿ ಡೇಟಾ ಸೈನ್ಸ್](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)
-[![ಆರಂಭಿಕರಿಗಾಗಿ AI](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)
-[![ಆರंभಿಕರಿಗಾಗಿ ಸೈಬರ್‌ಸುರಕ್ಷತೆ](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)
-[![ಆರಂಭಿಕರಿಗಾಗಿ ವೆಬ್ ಡೆವ್](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)
-[![ಆರಂಭಿಕರಿಗಾಗಿ IoT](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)
-[![ಆರಂಭಿಕರಿಗಾಗಿ XR ಅಭಿವೃದ್ಧಿ](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)
+### ಮೂಲ ಅಧ್ಯಯನ
+[![ಹೊಸವರಿಗಾಗಿ ML](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)
+[![ಹೊಸವರಿಗಾಗಿ ಡೇಟಾ ಸೈನ್ಸ್](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)
+[![ಹೊಸವರಿಗಾಗಿ AI](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)
+[![ಹೊಸವರಿಗಾಗಿ ಸೈಬರ್ ಜಾಗೃತಿ](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)
+[![ಹೊಸವರಿಗಾಗಿ ವೆಬ್ ಡೆವ್](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)
+[![ಹೊಸವರಿಗಾಗಿ IoT](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)
+[![ಹೊಸವರಿಗಾಗಿ XR ಡೆವಲಪ್ಮೆಂಟ್](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 ಸರಣಿ
-[![ಎಐ ಜೋಡಿಸಲಾದ ಪ್ರೋಗ್ರಾಮಿಂಗ್‌ಗಾಗಿ 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#/.NETಗಾಗಿ 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 ಸಾಹಸ](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)
+### ಕೋಟ್ಪೈಲಟ್ ಸರಣಿ
+[![AI ಜೋಡಿಸಿದ ಪ್ರೋಗ್ರಾಮಿಂಗ್‌ಗೆ ಕೋಟ್ಪೈಲಟ್](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#/.NETಗಾಗಿ ಕೋಟ್ಪೈಲಟ್](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)
+[![ಕೋಟ್ಪೈಲಟ್ ಸಾಹಸ](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 -->
 
-## ಸಹಾಯ ಪಡೆಯುವುದು
+## ಸಹಾಯ ಪಡೆಯುವಿಕೆ
 
-ನೀವು AI ಅಪ್ಲಿಕೇಶನ್‌ಗಳನ್ನು ನಿರ್ಮಿಸುವಾಗ ಅಡ್ಡಿಗಿಂತ ಬಿದ್ದಿದ್ದರೆ ಅಥವಾ ಯಾವುದೇ ಪ್ರಶ್ನೆಗಳಿದ್ದರೆ. MCP ಕುರಿತು ಚರ್ಚೆಗಳಲ್ಲಿ ಇತರೆ ಕಲಿಯುವವರು ಮತ್ತು ಅನುಭವಿಯಾದ ಡೆವಲಪರ್‌ಗಳ ಜೊತೆಗೆ ಸೇರಿ. ಪ್ರಶ್ನೆಗಳಿಗೆ ಸ್ವಾಗತ ಇರುವುದು ಮತ್ತು ಜ್ಞಾನವನ್ನು ಮುಕ್ತವಾಗಿ ಹಂಚಿಕೊಳ್ಳುವ ಬೆಂಬಲಾತ್ಮಕ ಸಮುದಾಯ ಇದಾಗಿದೆ.
+ನೀವು ಅಡುಕಾಗಿದ್ದೀರಾ ಅಥವಾ AI ಅಪ್ಲಿಕೇಶನ್ಗಳನ್ನು ನಿರ್ಮಿಸುವ ಬಗ್ಗೆ ಯಾವುದೇ ಪ್ರಶ್ನೆಗಳಿವೆ. MCP ಕುರಿತು ಚರ್ಚೆಗಳಲ್ಲಿ ಇತರ ಅಧ್ಯಯನಾರ್ಥಿಗಳು ಮತ್ತು ಅನುಭವಸंपನ್ನರನ್ನೂ ಸೇರಿ. ಇದು ಪ್ರಶ್ನೆಗಳಿಗೆ ಅವಕಾಶ ನೀಡುವ ಸಹಾಯಕ ಸಮುದಾಯ ಮತ್ತು ಜ್ಞಾನವನ್ನು ಮುಕ್ತವಾಗಿ ಹಂಚಿಕೊಳ್ಳಲಾಗುತ್ತದೆ.
 
-[![Microsoft Foundry ಡಿಸ್ಕೋರ್ಡ್](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![ಮೈಕ್ರೋಸಾಫ್ಟ್ ಫೌಂಡ್ರಿ ಡಿಸ್ಕಾರ್ಡ್](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ನೀವು ನಿರ್ಮಿಸುವಾಗ ಉತ್ಪನ್ನ ಫೀಡ್‌ಬ್ಯಾಕ್ ಅಥವಾ ದೋಷಗಳಿದ್ದರೆ, ದಯವಿಟ್ಟು ಭೇಟಿಮಾಡಿ:
+ನೀವು ಉತ್ಪನ್ನಪ್ರತೀಕಿರಣೆ ಅಥವಾ ನಿರ್ಮಾಣದ ಸಮಯದಲ್ಲಿ ದೋಷಗಳನ್ನು ನೋಡಿದರೆ ಭೇಟಿ ನೀಡಿ:
 
-[![Microsoft Foundry ಡೆವಲಪರ್ ಫೋರಂ](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)
+[![ಮೈಕ್ರೋಸಾಫ್ಟ್ ಫೌಂಡ್ರಿ ಡೆವಲಪರ್ ಫೋರಂ](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 -->
-ನಿರಾಕರಣೆ:
-ಈ ದಸ್ತಾವೇಜು AI ಅನುವಾದ ಸೇವೆ Co‑op Translator (https://github.com/Azure/co-op-translator) ಬಳಸಿ ಅನುವಾದಿಸಲಾಗಿದೆ. ನಾವು ನಿಖರತೆಯನ್ನು ಸಾಧಿಸಲು ಪ್ರಯತ್ನಿಸಿದರೂ, ಸ್ವಯಂಚಾಲಿತ ಅನುವಾದಗಳಲ್ಲಿ ದೋಷಗಳು ಅಥವಾ ಅಸ್ಪಷ್ಟತೆಗಳಿರಬಹುದೆಂದು ದಯವಿಟ್ಟು ಗಮನಿಸಿ. ಮೂಲ ಭಾಷೆಯಲ್ಲಿನ ದಸ್ತಾವೇಜನ್ನು ಅಧಿಕೃತ ಮೂಲವೆಂದು ಪರಿಗಣಿಸಬೇಕು. ಮಹತ್ವದ ಮಾಹಿತಿಗಾಗಿ ವೃತ್ತಿಪರ ಮಾನವ ಅನುವಾದವನ್ನು ಶಿಫಾರಸು ಮಾಡಲಾಗುತ್ತದೆ. ಈ ಅನುವಾದದ ಬಳಕೆಯಿಂದ ಉಂಟಾಗುವ ಯಾವುದೇ ತಪ್ಪು ಅರ್ಥಗ್ರಹಣೆಗಳು ಅಥವಾ ತಪ್ಪಾದ ವ್ಯಾಖ್ಯಾನಗಳಿಗಾಗಿ ನಾವು ಹೊಣೆಗಾರರಲ್ಲ.
+**ಅನ್ವಯಿಸಿ**:  
+ಈ ದಾಖಲೆ AI ಅನುವಾದ ಸೇವೆ [Co-op Translator](https://github.com/Azure/co-op-translator) ಬಳಸಿ ಅನುವಾದಿಸಲಾಗಿದೆ. ನಾವು ನಿಖರತೆಗಾಗಿ ಪ್ರಯತ್ನಿಸುತ್ತಿದ್ದರೂ, ಸ್ವಯಂಚಾಲಿತ ಅನುವಾದಗಳಲ್ಲಿ ದೋಷಗಳು ಅಥವಾ ಅಸತ್ಯತೆಗಳಿರಬಹುದು ಎಂದು ದಯವಿಟ್ಟು ಗಮನಿಸಿ. ಮೂಲ ಭಾಷೆಯಲ್ಲಿ ಇರುವ ಮೂಲ ದಾಖಲೆ ಅಧಿಕೃತ ಮೂಲ ಎಂದು ಪರಿಗಣಿಸಬೇಕು. ಅತಿ ಮಹತ್ವದ ಮಾಹಿತಿಗಾಗಿ, ವೃತ್ತಿಪರ ಮಾನವ ಅನುವಾದ ಶಿಫಾರಸು ಮಾಡಲಾಗಿದೆ. ಈ ಅನುವಾದ ಬಳಕೆಯಿಂದ ಉಂಟಾಗುವ ಯಾವುದೇ ತಪ್ಪು ಅರ್ಥದ ಅಥವಾ ತಪ್ಪು ವ್ಯಾಖ್ಯಾನಗಳಿಗಾಗಿ ನಾವು ಜವಾಬ್ದಾರಿಯಲ್ಲ.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/ml/README.md b/translations/ml/README.md
index da0943138..49d27801c 100644
--- a/translations/ml/README.md
+++ b/translations/ml/README.md
@@ -1,223 +1,232 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T14:35:06+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T18:00:18+00:00",
   "source_file": "README.md",
   "language_code": "ml"
 }
 -->
-[![GitHub ലൈസൻസ്](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub പങ്കാളികൾ](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub പ്രശ്നങ്ങൾ](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub പുൾ-റിക്വസ്റ്റുകൾ](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs സ്വാഗതം](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 വാച്ചർമാർ](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub ഫോർകുകൾ](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub സ്റ്റാർസ്](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/)
 
 ### 🌐 ബഹുഭാഷാ പിന്തുണ
 
-#### GitHub Action മുഖാന്തിരം (ആട്ടോമേറ്റഡ് & എല്ലായ്പ്പോഴും അപ്-ടു-ഡേറ്റ്) വഴി പിന്തുണയ്ക്കുന്നു
+#### GitHub ആക്ഷൻ വഴി പിന്തുണ (സ്വകാര്യവും എപ്പോഴും പുതിയതും)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[അറബി](../ar/README.md) | [ബെംഗാളി](../bn/README.md) | [ബൾഗേറിയൻ](../bg/README.md) | [ബർമീസ് (മ്യാൻമാർ)](../my/README.md) | [ചൈനീസ് (സിംപ്ലിഫൈഡ്)](../zh/README.md) | [ചൈനീസ് (ട്രാഡിഷണൽ, ഹോങ്കോങ്)](../hk/README.md) | [ചൈനീസ് (ട്രാഡിഷണൽ, മകാവോ)](../mo/README.md) | [ചൈനീസ് (ട്രാഡിഷണൽ, തായ്‍വാൻ)](../tw/README.md) | [ക്രൊവേഷ്യൻ](../hr/README.md) | [ചെക്](../cs/README.md) | [ഡാനിഷ്](../da/README.md) | [ഡച്ച്](../nl/README.md) | [എസ്റ്റോണിയൻ](../et/README.md) | [ഫിനിഷ്](../fi/README.md) | [ഫ്രഞ്ച്](../fr/README.md) | [ജർമ്മൻ](../de/README.md) | [ഗ്രീക്ക്](../el/README.md) | [ഹെബ്രോ](../he/README.md) | [ഹിന്ദി](../hi/README.md) | [ഹംഗേറിയൻ](../hu/README.md) | [ഇന്തോനേശ്യൻ](../id/README.md) | [ഇറ്റാലിയൻ](../it/README.md) | [ജാപ്പനീസ്](../ja/README.md) | [കന്നഡ](../kn/README.md) | [കൊറിയൻ](../ko/README.md) | [ലിതുവേനിയൻ](../lt/README.md) | [മലായ്](../ms/README.md) | [മലയാളം](./README.md) | [മറാത്തി](../mr/README.md) | [നെപ്പാളി](../ne/README.md) | [നായജീരിയൻ പിഡ്ജിൻ](../pcm/README.md) | [നോർവീജിയൻ](../no/README.md) | [പെഴ്സിയൻ (ഫാർസി)](../fa/README.md) | [പോളിഷ്](../pl/README.md) | [പോർച്ചുഗീസ് (ബ്രസീൽ)](../br/README.md) | [പോർച്ചുഗീസ് (പോർച്ചുഗൽ)](../pt/README.md) | [പഞ്ചാബി (ഗുരുമുഖി)](../pa/README.md) | [റൊമേനിയൻ](../ro/README.md) | [റഷ്യൻ](../ru/README.md) | [സെർബിയൻ (സിറിലിക്)](../sr/README.md) | [സ്ലോവാക്](../sk/README.md) | [സ്ലോവേനിയൻ](../sl/README.md) | [സ്പാനിഷ്](../es/README.md) | [സ്വാഹിലി](../sw/README.md) | [സ്വീഡിഷ്](../sv/README.md) | [ടാഗലോഗ് (ഫിലിപ്പീനോ)](../tl/README.md) | [തമിഴ്](../ta/README.md) | [തെലുങ്കു](../te/README.md) | [തായ്](../th/README.md) | [ടർക്കിഷ്](../tr/README.md) | [ഉക്രെയ്‌നിയൻ](../uk/README.md) | [ഉർദു](../ur/README.md) | [വിയറ്റ്നാമീസ്](../vi/README.md)
+[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)](../hk/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](./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)
+
+> **പ്രാദേശികമായി ക്ലോൺ ചെയ്യാൻ ഇഷ്ടമാണോ?**
+
+> ഈ റീപ്പോസിറ്ററിയിൽ 50+ ഭാഷാ തർജ്ജമകൾ ഉൾപ്പെടുന്നു, കൂടിയ ഡൗൺലോഡ് വലുപ്പം ഉണ്ടാക്കുന്നു. തർജ്ജമകൾ ഇല്ലാതെ ക്ലോൺ ചെയ്യാൻ, sparse checkout ഉപയോഗിക്കുക:
+> ```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'
+> ```
+> ഇത് നിങ്ങൾക്ക് കോഴ്സ് വളരെ വേഗത്തിൽ പൂർത്തിയാക്കാൻ ആവശ്യമുള്ള എല്ലാ വസ്തുക്കളും നല്കും.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-#### നമ്മുടെ സമൂഹത്തിൽ ചേർക്കൂ
+#### ഞങ്ങളുടെ സമൂഹത്തിലേക്ക് ചേരുക
 
-[![Microsoft Foundry ഡിസ്കോർഡ്](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)
 
-നമുക്കു Discord-ൽ "Learn with AI" സീരീസ് നീണ്ടുനടക്കുകയാണ്; കൂടുതല്‍ അറിയാന്‍ , 18 - 30 സെപ്റ്റംബർ, 2025 തീയതികൾക്കായി ഞങ്ങളോടൊപ്പം ചേരൂ: [Learn with AI Series](https://aka.ms/learnwithai/discord). GitHub Copilot ഉപയോഗിച്ച് Data Science ചെയ്യാനുള്ള ടിപ്സ്-ട്രിക്ക്‌സ് നിങ്ങൾക്ക് ലഭിക്കും.
+നമ്മൾ ഒരു ഡിസ്‌കോർഡിൽ "AI സീരീസുമായി പഠിക്കുക" നടന്നുകൊണ്ടിരിക്കുന്നു; കൂടുതൽ അറിയാനും 18 - 30 സെപ്റ്റംബർ, 2025 ഇൽ ഞങ്ങളോടൊപ്പം ചേരാനും [Learn with AI Series](https://aka.ms/learnwithai/discord) സന്ദർശിക്കുക. GitHub Copilot ഉപയോഗിച്ച് Data Science-നുള്ള ടിപ്‌സും ട്രിക്കുകളും നിങ്ങൾക്ക് ലഭിക്കും.
 
-![Learn with AI series](../../translated_images/ml/3.9b58fd8d6c373c20.webp)
+![Learn with AI series](../../../../translated_images/ml/3.9b58fd8d6c373c20.webp)
 
-# Machine Learning for Beginners - A Curriculum
+# തുടക്കക്കാർക്കുള്ള മെഷീൻ ലേണിംഗ് - ഒരു പാഠ്യപദ്ധതി
 
-> 🌍 ലോക വിഭവങ്ങളിലൂടെ (world cultures) ഞങ്ങൾ മെഷീൻ ലേണിംഗ് ആഴത്തില്‍ അന്വേഷിക്കുന്നതിനൊടുവിലാണ് ഈ യാത്ര 🌍
+> 🌍 ലോക സംസ്കാരങ്ങളിലൂടെ മെഷീൻ ലേണിംഗ് അന്വേഷിച്ച് ലോകമെടുത്ത് സഞ്ചരിക്കുക 🌍
 
-Microsoft-ലെ Cloud Advocates-ുകൾ സന്തോഷത്തോടെ ഒരു 12-വാര, 26-പാഠങ്ങളുടെ പാഠ്യപദ്ധതി അവതരിക്കുന്നു — പരമാവധി ഫോകസ് ചെയ്യുന്നത് **Machine Learning** എന്നതിലാണി. ഈ പാഠ്യപദ്ധതിയിൽ, നിങ്ങൾ പലപ്പോഴും **classic machine learning** എന്ന് വിളിക്കപ്പെടുന്ന കാര്യങ്ങളെക്കുറിച്ച് പഠിക്കും, പ്രധാനമായി Scikit-learn ലൈബ്രറി ഉപയോഗിച്ച്, ആഴത്തിലുള്ള പഠനം (deep learning) ഒഴിവാക്കി; ആ ഭാഗം നമ്മുടെ [AI for Beginners' curriculum](https://aka.ms/ai4beginners) ൽ ഉൾപ്പെടുത്തിയിട്ടുണ്ട്. ഈ പാഠങ്ങൾ ഞങ്ങളുടെ ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) നെ കൂടാതെ ഉപയോഗിക്കാവുന്നതാണ്.
+Microsoft-ലെ ക്ലൗഡ് അഡ്വക്കേറ്റ്‌സ് 12 ആഴ്ച, 26 പാഠങ്ങളുള്ള **മെഷീൻ ലേണിംഗ്** കോഴ്‌സ് എത്തിക്കുകയാണ്. ഈ പാഠ്യപദ്ധതിയിൽ, പ്രധാനമായും Scikit-learn ലൈബ്രറി ഉപയോഗിച്ച് ചിലപ്പോഴൊക്കെ **പാരമ്പര്യ മെഷീൻ ലേണിംഗ്** എന്ന് പറയപ്പെടുന്നതിനെക്കുറിച്ചാണ് പഠിക്കുന്നത്, ഡീപ് ലേണിംഗ് ഒഴിവാക്കുന്നു, അത് ഞങ്ങളുടെ [AI for Beginners' curriculum](https://aka.ms/ai4beginners)-ൽ ഉൾപ്പെടുത്തിയിട്ടുണ്ട്. ഈ പാഠങ്ങളെ ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners) പോലുള്ളവയുമായും ചേർത്തുപയോഗിക്കാം.
 
-ലോകമാകെയുള്ള ഡാറ്റാ സ്രോതസ്സുകളിൽ ക്ലാസിക്കൽ സാങ്കേതികവിദ്യകൾ പ്രയോഗിക്കുമ്പോൾ ഞങ്ങളോടൊപ്പം യാത്ര ചെയ്യൂ. ഓരോ പാഠത്തിനും pre- and post-lesson quizzes ഉം, എഴുത്തായ നിർദ്ദേശങ്ങളും, ഒരു സൊല്യൂഷനും, അസൈൻമെന്റും, കൂടാതെ മറ്റും ഉൾപ്പെടുത്തിയിട്ടുണ്ട്. പ്രോജെക്ട്-ആധാരിത പഠനരീതിയിലൂടെ നിങ്ങൾ നിര്‍മിക്കുമ്പോൾ പഠിക്കുന്നതാണ്; ഇത് പുതിയ കഴിവുകൾ 'സ്ഥിരമാക്കുന്നതിനുള്ള' തെളിയിച്ച രീതിയാണ്.
+ലോകമാകെ ഏറിയ സ്ഥലങ്ങളിൽ നിന്നുള്ള ഡാറ്റ ഉപയോഗിച്ച് ഈ പാരമ്പര്യ സാങ്കേതിക വിദ്യകൾ ഞങ്ങൾ ഉപയോഗിക്കും. ഓരോ പാഠത്തിനും മുൻപും കഴിഞ്ഞും ക്വിസുകൾ, എഴുത്തുകൂടിയ നിർദ്ദേശങ്ങൾ, സമാധാനം, അസൈൻമെന്റ് എന്നിവ ഉൾപ്പെടുന്നു. നമ്മുടെ പ്രൊജക്ട് അടിസ്ഥാനത്തിലുള്ള പഠനരീതി നിങ്ങളെ നിർമാണത്തിലൂടെ പഠിപ്പിക്കും, പുതിയ കഴിവുകൾ പിടിച്ചുപറ്റാൻ സഹായിക്കുന്ന മറ്യാദമായ മാർഗ്ഗമാണ്.
 
-**✍️ наших ആർഹതക്കാര്‍ക്ക് ഹൃദയം നിറഞ്ഞ നന്ദി** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ ഞങ്ങളുടെ എഴുത്തുകാർക്ക് ആത്മാർത്ഥ നന്ദി:** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu, Amy Boyd
 
-**🎨 ഞങ്ങളുടെ ചിത്രകാരന്മാർക്കും നന്ദി** Tomomi Imura, Dasani Madipalli, and Jen Looper
+**🎨 ചിത്രകലാകാരന്മാർക്കുമാണ് നന്ദി:** Tomomi Imura, Dasani Madipalli, Jen Looper
 
-**🙏 പ്രത്യേക നന്ദി 🙏 നമ്മുടെ Microsoft Student Ambassador രചയിതാക്കൾക്കും, റിവ്യൂവേർമാർക്കും, ഉള്ളടക്ക സംഭാവനക്കാർക്കും**, പ്രത്യേകിച്ച് Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+**🙏 പ്രത്യേക നന്ദി 🙏 Microsoft Student Ambassador എഴുത്തുകാർ, റിവ്യൂവേഴ്സ്, ഉള്ളടക്ക സംഭാവകർക്ക്**, പ്രത്യേകിച്ച് Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, Snigdha Agarwal
 
-**🤩 R പാഠങ്ങൾക്കുള്ള വളരെയധികം നന്ദി Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta-ന്!**
+**🤩 Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, Vidushi Gupta-യുടെ R പാഠങ്ങൾക്കായുള്ള കൂടുതൽ കൃതജ്ഞത!**
 
 # ആരംഭിക്കുന്നത്
 
-ഈ ചുവടുകൾ പിന്തുടരുക:
-1. **റിപ്പോസിറ്ററി Fork ചെയ്യുക**: ഈ പേജിന്റെ മുകളിൽ വലത്തുഭാഗത്ത് ഉള്ള "Fork" ബട്ടണിൽ ക്ലിക്ക് ചെയ്യുക.
-2. **റിപ്പോസിറ്ററി ക്ലോൺ ചെയ്യുക**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+ഈ ഘട്ടങ്ങൾ പിന്തുടരുക:
+1. **റീപ്പോസിറ്ററി Fork ചെയ്യുക**: ഈ പേജിന്റെ മുകളിൽ വലതു കോണിൽ തിരഞ്ഞെടുത്ത "Fork" ബട്ടൺ ക്ലിക്ക് ചെയ്യുക.
+2. **റീപ്പോസിറ്ററി ക്ലോൺ ചെയ്യുക**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [ഈ കോഴ്‌സിന് ആവശ്യമായ എല്ലാ അധിക സാമ്പത്തിക വనറുകൾ ഞങ്ങളുടെ Microsoft Learn ശേഖരത്തിൽ കാണുക](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [ഈ കോഴ്‌സിനുള്ള എല്ലാ അധികവസ്തുക്കളും ഞങ്ങളുടെ Microsoft Learn ശേഖരത്തിൽ കാണുക](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **സഹായം വേണമോ?** ഇൻസ്റ്റലേഷൻ, സജ്ജീകരണം, പാഠങ്ങൾ റൺ ചെയ്യുന്നതുമായി ബന്ധപ്പെട്ട പൊതു പ്രശ്നങ്ങൾക്ക് പരിഹാരങ്ങൾക്കായി ഞങ്ങളുടെ [Troubleshooting Guide](TROUBLESHOOTING.md) പരിശോധിക്കുക.
+> 🔧 **സഹായം വേണോ?** ഇൻസ്റ്റലേഷൻ, സജ്ജീകരണം, പാഠങ്ങൾ പ്രവർത്തിപ്പിക്കുന്നതിലുള്ള സാധാരണ പ്രശ്നങ്ങൾക്ക് ഞങ്ങളുടെ [Troubleshooting Guide](TROUBLESHOOTING.md) സന്ദർശിക്കുക.
 
 
-**[Students](https://aka.ms/student-page)**, ഈ പാഠ്യപദ്ധതി ഉപയോഗിക്കാൻ, മുഴുവൻ റെപ്പോ നിങ്ങളുടെ സ്വന്തം GitHub അക്കൗണ്ടിലേക്ക് fork ചെയ്ത് താങ്കൾ തനിക്കോ അല്ലെങ്കിൽ ഒരു ഗ്രൂപ്പിനൊത്ത് ചേർന്ന് വ്യായാമങ്ങൾ പൂർത്തിയാക്കുക:
+**[വിദ്യാർത്ഥികൾ](https://aka.ms/student-page)**, ഈ പാഠ്യപദ്ധതി ഉപയോഗിക്കാനായി, മുഴുവൻ രീപ്പോ നിങ്ങൾ സ്വന്തം GitHub അക്കൗണ്ടിലേക്ക് fork ചെയ്ത് സ്വതന്ത്രമായി അല്ലെങ്കിൽ ഗ്രൂപ്പുമായി അഭ്യാസങ്ങൾ പൂർത്തിയാക്കുക:
 
-- ഒരു pre-lecture quiz-ൽ തുടങ്ങുക.
-- ലക്ചർ വായിച്ച് പ്രവർത്തനങ്ങൾ പൂർത്തിയാക്കുക, ഓരോ knowledge check-ൽ നില്‍ക്കുകയും ആലോചിക്കുകയും ചെയ്യുക.
-- സൊല്യൂഷൻ കോഡ് ഓടിക്കാതെ പാഠങ്ങൾ മനസ്സിലാക്കി പ്രോജക്ടുകൾ സൃഷ്ടിക്കാൻ ശ്രമിക്കുക; എന്നിരുന്നാലും ആ കോഡ് ഓരോ project-ഓറിയന്റഡ് പാഠത്തിനും ഉള്ള `/solution` സഹ ഫോളഡറുകളിൽ ലഭ്യമായിരിക്കും.
-- post-lecture quiz സ്വീകരിക്കുക.
-- ചലഞ്ച് പൂർത്തിയാക്കുക.
-- അസൈൻമെന്റ് പൂർത്തിയാക്കുക.
-- ഒരു പാഠ ഗ്രൂപ്പ് പൂർത്തിയാക്കിയതിന് ശേഷം, [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) സന്ദർശിച്ച് പരിച്ഛേദം പങ്കുവെയ്ക്കുക மற்றும் അനുയോജ്യമായ PAT റൂബ്രിക്ക് പൂർത്തിയാക്കി "learn out loud" ചെയ്യുക. 'PAT' എന്നത് Progress Assessment Tool-ആണ്; ഇത് നിങ്ങൾ ഭാവിപ്പാഠ്യകൗശലങ്ങൾ മെച്ചപ്പെടുത്താൻ ഉപയോഗിക്കുന്ന ഒരു റൂബ്രിക്കാണ്. മറ്റുള്ളവരുടെ PAT-കളിലും പ്രതികരിക്കാൻ സാധിക്കും; ഇപ്രകാരം നാം ഒരുമിച്ച് പഠിക്കാം.
+- പ്രാക്ടിക്കൽ ക്വിസ് ആരംഭിക്കുക.
+- ലക്ചർ വായിച്ച് പ്രവർത്തനങ്ങൾ തീർക്കുക, ഓരോ നോളജ് ചെക്കിലും reflecting ചെയ്യുക.
+- ലോണുകളെ മനസ്സിലാക്കി പ്രൊജക്ടുകൾ നിർമ്മിക്കാൻ ശ്രമിക്കുക; എങ്കിലും കോഡ് ഓരോ പ്രൊജക്ട് പാഠഭാഗത്തിലും /solution ഫോൾഡറിൽ ലഭ്യമാണ്.
+- പാഠം കഴിഞ്ഞ് പോസ്റ്റ്-ലക്ചർ ക്വിസ് എഴുതുക.
+- ചാലഞ്ച് പൂർത്തിയാക്കുക.
+- അസൈൻമെന്റ് ചെയ്യുക.
+- ഒരു പാഠസമൂഹം പൂർത്തിയാക്കിയ ശേഷം, [Discussion Board](https://github.com/microsoft/ML-For-Beginners/discussions) സന്ദർ‍ശിച്ച് സാധ്യതകൾ പങ്കുവെക്കുക. പാറ്റ് (PAT - Progress Assessment Tool) പൂരിപ്പിച്ച് കൂടെ പഠിക്കുക. മറ്റുള്ളവരുടെ PAT-കളിൽ പ്രതികരിക്കുകയും ചെയ്യാം.
 
-> കൂടുതൽ പഠനത്തിന്, ഞങ്ങൾ ഇവ നിർദേശിക്കുന്നു: [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) മോഡ്യൂളുകൾ आणि ലേണിംഗ് പാതകൾ പിന്തുടരുക.
+> കൂടുതൽ പഠനത്തിന്, ഞങ്ങളുടെ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) മോഡ്യൂളുകളും പഠനപാതകളും ഫോളോ ചെയ്യാൻ ഞങ്ങൾ ശുപാർശ ചെയ്യുന്നു.
 
-**ഉപാധ്യാപകർ**, ഈ പാഠ്യപദ്ധതി എങ്ങനെ ഉപയോഗിക്കാമെന്നതിന്റെ കുറിച്ച് ഞങ്ങൾ [കുറിപ്പ് നൽകിയിട്ടുണ്ട്](for-teachers.md).
+**അധ്യാപകർക്ക്**, ഈ പാഠ്യപദ്ധതിയിൽ ഉപയോഗിക്കാനുള്ള ചില നിർദ്ദേശങ്ങൾ ഞങ്ങൾ [ഇവിടെ](for-teachers.md) ഉൾപ്പെടുത്തിയിട്ടുണ്ട്.
 
 ---
 
-## വീഡിയോ വെൽക്കോുതുകൾ
+## വീഡിയോ വോക്-തറൂ
 
-ചില പാഠങ്ങൾ ചെറിയ ഫോർമാറ്റിലുള്ള വീഡിയോ ആയി ലഭ്യമാണ്. ഈ മുഴുവൻ വിഡിയോകൾ പാഠങ്ങളിൽ തന്നെ ഇൻ-ലൈൻ ആയി കാണാനോ, അല്ലെങ്കിൽ [ML for Beginners പ്ലേലിസ്റ്റ് - Microsoft Developer YouTube ചാനൽ](https://aka.ms/ml-beginners-videos) ൽ ചിത്രം ക്ലിക്കുചെയ്ത് കാണാവുന്നതാണ്.
+ഒരു ചെറിയ വീഡിയോ രൂപത്തിൽ ചില പാഠങ്ങൾ ലഭ്യമാണ്. ഈ വീഡിയോകൾ പാഠങ്ങൾക്കுள் കാണാനോ, [Microsoft Developer YouTube ചാനലിലെ ML for Beginners Playlist](https://aka.ms/ml-beginners-videos) ൽനിന്നും ലഭ്യമാണ്. അടിഭാഗത്തെ ചിത്രത്തിൽ ക്ലിക്ക് ചെയ്യുക.
 
-[![ML for beginners ബാനർ](../../translated_images/ml/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/ml/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## ടീം കാണുക
+## ടീമിനെ പരിചയപ്പെടുക
 
-[![പ്രമോ വീഡിയോ](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**ജിഫ് ഒരുക്കിയത്** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**ഗിഫ് by** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 പ്രോജക്ടിനെப் பற்றിയും അതിനെ സൃഷ്ടിച്ചവരെക്കുറിച്ചുള്ള വീഡിയോ കാണാൻ മുകളിൽ ഉള്ള ചിത്രം ക്ലിക്ക് ചെയ്യൂ!
+> 🎥 ഈ ചിത്രത്തിൽ ക്ലിക്ക് ചെയ്ത് പ്രോജക്ടിന്റെയും സൃഷ്ടകരുടെയും കുറിച്ച് വീഡിയോ കാണുക!
 
 ---
 
-## ശൈലി-രീതികൾ (Pedagogy)
+## പഠനരീതി
 
-ഈ പാഠ്യപദ്ധതി നിർമ്മിച്ചുകൊണ്ടിരിക്കുമ്പോൾ ഞങ്ങൾ രണ്ട് അധ്യാപന സിദ്ധാന്തങ്ങൾ തിരഞ്ഞെടുക്കുകയും ചെയ്തിട്ടുണ്ട്: കൈകൊണ്ടുള്ള രീതിയായത് (hands-on) എന്ന് ഉറപ്പാക്കുന്ന **project-based** ആയതും, കൂടാതെ അതിൽ **സഹജമായ കുറേക്വിസുകൾ (frequent quizzes)** ഉണ്ടായിട്ടുള്ളതുമാണ്. കൂടാതെ ഈ പാഠ്യപദ്ധതിക്ക് ഒരു പൊതു **തീം** കൂടി നൽകിയിട്ടുണ്ട്, ഇതു ഒരു ഏകോപനം നൽകാൻ സഹായിക്കുന്നു.
+ഈ പാഠ്യപദ്ധതി രൂപകൽപ്പന ചെയ്യുമ്പോൾ രണ്ട് പഠന തത്വങ്ങൾ ഞങ്ങൾ തിരഞ്ഞെടുക്കുന്നു: അല്പം-കൈയിൽപിടിക്കുന്ന **പ്രൊജക്ട് അടിസ്ഥാനപരമായ**തും, **നിരന്തരമായ ക്വിസുകളും** ഉള്ളതും. കൂടാതെ, ഇത് സംയോജിപ്പിക്കാനുള്ള ഒരു പൊതുസംബന്ധമായ **വിഷയം** ഉണ്ട്.
 
-സാമ്പത്തികമായും കാര്യക്ഷമമായും ഉള്ളടക്കം പ്രോജെക്ടുകളുമായി ബന്ധിപ്പിക്കുമ്പോൾ വിദ്യാർത്ഥികളുടെ മൊഹവും ശ്രദ്ധയും വർദ്ധിക്കുന്നു, ആശയങ്ങളുടെ retention മെച്ചപ്പെടും. ക്ലാസിനു മെൽലേര്‍ച്ചയില്ലാത്ത ഒരു quiz പഠിപ്പിക്കുന്നതിനുള്ള ലക്ഷ്യം സജ്ജമാക്കുന്നു, mentre ക്ലാസിനു ശേഷം വരുന്ന രണ്ടാമത്തെ quiz കൂടുതൽ retention ഉറപ്പാക്കും. ഈ പരിപാടി പ്രാവിന് അനുസരിച്ച് ലവിച്ചും രസകരവുമാണ്; പൂർണ്ണമായോ ഭാഗികമായോ നിങ്ങൾക്ക് സ്വീകരിക്കാവുന്നതാണ്. പ്രോജെക്ടുകൾ ചെറിയതിൽ ആരംഭിച്ച് 12-വാരക്കാലയളവിന്റെ അവസാനം വരെ ക്രമേണ സങ്കീർണ്ണമാവും. ഈ പാഠ്യപദ്ധതിയിൽ ML-ന്റെ യാഥാർഥ്യ ലോക പ്രയോഗങ്ങളെക്കുറിച്ചുള്ള ഒരു പോസ്ട്സ്ക്രിപ്റ്റും ഉൾപ്പെടുത്തിയിട്ടുണ്ട്, ഇത് കൂടുതൽ ക്രെഡിറ്റ് ലഭിക്കാൻ അല്ലെങ്കിൽ ചർച്ചയ്ക്കുള്ള അടിസ്ഥാനമായ് ഉപയോഗിക്കാവുന്നതാണ്.
+വിഷയങ്ങൾ പ്രൊജക്ടുകളുമായി ഒത്തുചേരുന്നതിനാൽ വിദ്യാർത്ഥികൾ കൂടുതൽ ആകർഷകമായി പഠിച്ചു ആശയങ്ങൾ ദീർഘകാലം മനസ്സിലാക്കും. കുറഞ്ഞ സാഹചര്യത്തിലുള്ള ഒരു ക്വിസ് ക്ലാസിനായി ഒരുക്കുന്നതിനും പഠനത്തിനും ഉദ്ദേശ്യം സജ്ജമാക്കുന്നതിനും ഉപയോഗിക്കുന്നു; ക്ലാസ് കഴിഞ്ഞ് മറ്റൊരു ക്വിസ് അറിവ് ഉറപ്പാക്കുന്നു. ഈ പാഠ്യപദ്ധതി സ്വൈര്യവും രസം നിറഞ്ഞതുമായതാണ്, മുഴുവനായോ ഭാഗികമായോ ഇതുപയോഗിക്കാം. പ്രൊജക്ടുകൾ ചെറിയതിൽ നിന്നും ആരംഭിച്ച് 12 ആഴ്ചകളിലെ അവസാനത്തോടെ കൂടുതൽ സങ്കീർണ്ണമാകും. ഈ പാഠ്യപദ്ധതിയിൽ മെഷീൻ ലേണിംഗിന്റെ യാഥാർത്ഥ്യ പ്രയോഗങ്ങൾ പരാമർശിക്കുന്ന ഒരു പങ്ക് ഉൾക്കൊള്ളുന്നു; ഇത് അധികശ്രോതസായി അല്ലെങ്കിൽ ചർച്ചയ്ക്കായി ഉപയോഗിക്കാം.
 
-> ഞങ്ങളുടെ [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), এবং [Troubleshooting](TROUBLESHOOTING.md) മാർഗനിർദ്ദേശങ്ങൾ കാണുക. നിങ്ങളുടെ നിർമാതൃകമുള്ള ഫീഡ്‌ബാക്ക് ഞങ്ങൾ സ്വാഗതം ചെയ്യുന്നു!
+> ഞങ്ങളുടെ [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), [Troubleshooting](TROUBLESHOOTING.md) മാർഗ്ഗരേഖകൾ കാണുക. നിങ്ങളുടെ നിർമ്മിതാത്മക പ്രതികരണങ്ങൾ ഞങ്ങൾ സ്വാഗതം ചെയ്യുന്നു!
 
-## ഓരോ പാഠത്തിലും ഉൾപ്പെടുന്നത്
+## ഓരോ പാഠവും ഉൾക്കൊള്ളുന്നത്
 
-- ഐച്ഛിക sketchnote
-- ഐച്ഛിക കൂടുതൽ വീഡിയോകൾ
-- വീഡിയോ വാക്ക്-ത്രൂ (ചില പാഠങ്ങൾ മാത്രം)
-- [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
-- എഴുത്തായ പാഠം
-- പ്രോജെക്ട്-അധിഷ്ഠിത പാഠങ്ങളിലൊക്കെയാണ്, പ്രോജക്ട് നിർമ്മിക്കുന്നതിന്‍റെ ഘട്ടം-ഘട്ടമായും ഗൈഡുകൾ
-- knowledge checks
-- ഒരു ചലഞ്ച്
-- അനുബന്ധ വായന
+- ഐച്ഛിക സ്കെച് നോട്ട്
+- ഐച്ഛിക അധിക വീഡിയോ
+- വീഡിയോ വോക്-തറൂ (ചില പാഠങ്ങൾക്കായി മാത്രം)
+- [പ്രി-ലെക്ചർ വാർമപ്പ് ക്വിസ്](https://ff-quizzes.netlify.app/en/ml/)
+- എഴുത്തുപാഠം
+- പ്രൊജക്ട്-അധിഷ്ടിത പാഠങ്ങൾക്ക്, പ്രൊജക്ട് നിർമ്മിക്കുന്നതിനുള്ള ഘട്ടംഘട്ടമായ മാർഗ്ഗങ്ങൾ
+- അറിവ് പരിശോധനകൾ
+- ഒരു ചാലഞ്ച്
+- അധിക വായന
 - അസൈൻമെന്റ്
-- [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
-
-> **ഭാഷകളെക്കുറിച്ചുള്ള ഒരു കുറിപ്പ്**: ഈ പാഠങ്ങൾ പ്രധാനമായും Python-ൽ എഴുതിയതാണെങ്കിലും, പലതും R-യിലും ലഭ്യമാണ്. R പാഠം പൂർത്തിയാക്കാൻ, `/solution` ഫോൾഡർ കാണുക, അവിടെയുണ്ടാകുന്ന R പാഠങ്ങൾക്കായ് നോക്കുക. അവയ്ക്ക് .rmd എക്സ്റ്റൻഷൻ ഉണ്ടായിരിക്കും, ഇത് ഒരു **R Markdown** ഫയൽക്ക് പ്രതീകമാണ് — ഇത് `code chunks` (R അല്ലെങ്കിൽ മറ്റ് ഭാഷകളിലെ) ഒപ്പം ഒരു `YAML header` (PDF പോലുള്ള ഔട്ട്പുട്ടുകൾ എങ്ങിനെ ഫോർമാറ്റ് ചെയ്യാമെന്ന് മാർഗനിർദ്ദേശം നൽകുന്നു) ഒരു `Markdown document`-ലാണ് ഉൾപ്പെടുത്തുന്നത്. അതുകൊണ്ട്, ഇത് ഡാറ്റാ സയൻസിന്‍റെ മികച്ച ഒരു കെട്ടിടരചനാ ഫ്രെയിംവർക്കായി സേവിക്കുന്നു — നിങ്ങളുടെ കോഡ്, അതിന്റെ ഔട്ട്പുട്ട്, നിങ്ങളുടെ ചിന്തകൾ എന്നിവ എല്ലാം Markdown-ൽ എഴുതി ഫലമായി കാണിക്കാനുള്ള സംവിധാനമാണ്. കൂടാതെ, R Markdown ഡോക്യുമെന്റുകൾ PDF, HTML, അല്ലെങ്കിൽ Word പോലുള്ള ഔട്ട്പുട്ട് ഫോർമാറ്റുകളിലേക്ക് റെൻഡർ ചെയ്യാൻ കഴിയും.
-
-> **ക്വിസുകൾ സംബന്ധിച്ച ഒരു കുറിപ്പ്**: എല്ലാ ക്വിസുകളും [Quiz App folder](../../quiz-app) ൽ ഉൾപ്പെടുത്തിയിട്ടുണ്ട് — ആകെ 52 ക്വിസുകൾ, ഓരോന്നിലും മൂന്ന് ചോദ്യങ്ങളാണ്. അവ പാഠങ്ങളിൽ നിന്ന് ലിങ്കുചെയ്യപ്പെട്ടിട്ടുണ്ട്; എന്നാൽ quiz ആപ്പ് ലോക്കലായി ഓടിക്കാൻ കഴിയും; ലോക്കലായി ഹോസ്റ്റ് ചെയ്യാനോ Azure-ലേക്ക് ഡിപ്ലോയിക്കാനോ `quiz-app` ഫോൾഡറിലെ നിർദ്ദേശങ്ങൾ പിന്തുടരുക.
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-| പാഠം നമ്പർ |                             വിഷയം                              |                   പാഠം വിഭാഗം                   | അഭ്യസന ലക്ഷ്യങ്ങൾ                                                                                                             |                                                              ലിങ്ക് ചെയ്ത പാഠം                                                               |                        രചയിതാവ്                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                മെഷീൻ ലേണിങിന്റെ പരിചയം                |      [പരിചയം](1-Introduction/README.md)       | മെഷീൻ ലേണിങ്ങിന്റെ അടിസ്ഥാന ധാരണകൾ പഠിക്കുക                                                                                |                                             [പാഠം](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                മെഷീൻ ലേണിങ്ങിന്റെ ചരിത്രം                 |      [പരിചയം](1-Introduction/README.md)       | ഈ മേഖലയ്ക്കുള്ള അടിസ്ഥാന ചരിത്രം പഠിക്കുക                                                                                         |                                            [പാഠം](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 നീതിയും മെഷീൻ ലേണിങ്ങും                  |      [പരിചയം](1-Introduction/README.md)       | എംഎൽ മോഡലുകൾ നിർമ്മിക്കആരും പ്രയോഗിക്കാറുമ്പോൾ വിദ്യാർത്ഥികൾ പരിഗണിക്കേണ്ട പ്രധാന ദാർശനിക പ്രശ്നങ്ങൾ എന്തൊക്കെയാണ്? |                                              [പാഠം](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                മെഷീൻ ലേണിംഗ് ടെക്നിക്കുകൾ                 |      [പരിചയം](1-Introduction/README.md)       | എംഎൽ ഗവേഷകർ എംഎൽ മോഡലുകൾ നിർമ്മിക്കാൻ ഏത് സാങ്കേതിക വിദ്യകൾ ഉപയോഗിക്കുന്നു?                                                                       |                                          [പാഠം](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   റെഗ്രഷനിലേക്ക് പരിചയം                   |        [റെഗ്രഷൻ](2-Regression/README.md)         | റെഗ്രഷൻ മോഡലുകൾക്കായി Python, Scikit-learn എന്നിവ ഉപയോഗിച്ച് ആരംഭിക്കുക                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                നോർത്ത് അമേരിക്കൻ പംപ്കിൻ വിലകൾ 🎃                |        [റെഗ്രഷൻ](2-Regression/README.md)         | എംഎലിന് തയ്യാറെടുക്കലായി ഡാറ്റ വിന്യസിക്കുകയും ശുദ്ധീകരിക്കുകയും ചെയ്യുക                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                നോർത്ത് അമേരിക്കൻ പംപ്കിൻ വിലകൾ 🎃                |        [റെഗ്രഷൻ](2-Regression/README.md)         | ലീനിയർ ಮತ್ತು പോളിനോമിയൽ റെഗ്രഷൻ മോഡലുകൾ നിർമ്മിക്കുക                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                നോർത്ത് അമേരിക്കൻ പംപ്കിൻ വിലകൾ 🎃                |        [റെഗ്രഷൻ](2-Regression/README.md)         | ഒരു ലൊജിസ്റ്റിക് റെഗ്രഷൻ മോഡൽ നിർമ്മിക്കുക                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          ഒരു വെബ് ആപ്പ് 🔌                          |           [വെബ് ആപ്പ്](3-Web-App/README.md)            | നിങ്ങളുടെ പരിശീലിപ്പിച്ച മോഡൽ ഉപയോഗിക്കാൻ ഒരു വെബ് ആപ്പ് നിർമ്മിക്കുക                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 ക്ലാസിഫിക്കേഷനിലേക്ക് പരിചയം                 |    [ക്ലാസിഫിക്കേഷൻ](4-Classification/README.md)     | ഡാറ്റ ശുദ്ധമാക്കുക, തയ്യാറാക്കുക, വിന്യസിക്കുക; ക്ലാസിഫിക്കേഷനിലേക്ക് പരിചയം                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             സ്വാദിഷ്ടമായ ഏഷ്യൻ, ഇന്ത്യൻ ഭക്ഷണങ്ങൾ 🍜             |    [ക്ലാസിഫിക്കേഷൻ](4-Classification/README.md)     | ക്ലാസിഫയറുകളിലേക്ക് പരിചയം                                                                                                     | [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       |             സ്വാദിഷ്ടമായ ഏഷ്യൻ, ഇന്ത്യൻ ഭക്ഷണങ്ങൾ 🍜             |    [ക്ലാസിഫിക്കേഷൻ](4-Classification/README.md)     | കൂടുതൽ ക്ലാസിഫയറുകൾ                                                                                                                | [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       |             സ്വാദിഷ്ടമായ ഏഷ്യൻ, ഇന്ത്യൻ ഭക്ഷണങ്ങൾ 🍜             |    [ക്ലാസിഫിക്കേഷൻ](4-Classification/README.md)     | നിങ്ങളുടെ മോഡൽ ഉപയോഗിച്ച് ഒരു റികമൻഡർ വെബ് ആപ്പ് നിർമ്മിക്കുക                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   ക്ലസ്റ്ററിംഗിലേക്ക് പരിചയം                   |        [ക്ലസ്റ്ററിംഗ്](5-Clustering/README.md)         | ഡാറ്റ ശുദ്ധമാക്കുക, തയ്യാറാക്കുക, വിന്യസിക്കുക; ക്ലസ്റ്ററിംഗിലേക്ക് പരിചയം                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              നൈജീരിയന്‍ സംഗീതസ്വാദുകൾ ഗവേഷിക്കുക 🎧              |        [ക്ലസ്റ്ററിംഗ്](5-Clustering/README.md)         | K-Means ക്ലസ്റ്ററിംഗ് രീതീ പരിശോധിക്കുക                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിങ്ങിലേക്ക് പരിചയം ☕️         |   [സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ്](6-NLP/README.md)    | ഒരു ലളിതമായ ബോട്ട് നിർമ്മിച്ച് NLP-ന്റെ അടിസ്ഥാനങ്ങൾ പഠിക്കുക                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      സാധാരണ NLP ജോലികൾ ☕️                      |   [സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ്](6-NLP/README.md)    | ഭാഷാ ഘടനകൾ കൈകാര്യം ചെയ്യുമ്പോൾ ആവശ്യമുള്ള സാധാരണ ജോലികൾ മനസ്സിലാക്കി നിങ്ങളുടെ NLP അറിവ് ആഴമാക്കുക                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             പരിഭാഷയും സെൻറ്റിമെന്റ് വിശകലനവും ♥️              |   [സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ്](6-NLP/README.md)    | ജെയിൻ ഓസ്റ്റെൻ ഉപയോഗിച്ച് പരിഭാഷയും സെൻറ്റിമെന്റ് വിശകലനവും                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  യൂറോപ്പിലെ റൊമാന്റിക് ഹോട്ടലുകൾ ♥️                  |   [സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ്](6-NLP/README.md)    | ഹോട്ടൽ റിവ്യൂകളുമായി സെൻറ്റിമെന്റ് വിശകലനം 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  യൂറോപ്പിലെ റൊമാന്റിക് ഹോട്ടലുകൾ ♥️                  |   [സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ്](6-NLP/README.md)    | ഹോട്ടൽ റിവ്യൂകളുമായി സെൻറ്റിമെന്റ് വിശകലനം 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            ടൈം സീരീസുകൾ പ്രവചിക്കുന്നതിന്റെ പരിചയം             |        [ടൈം സീരീസ്](7-TimeSeries/README.md)        | ടൈം സീരീസ് ഫോറികാസ്റ്റിങ്ങിന്റെ പരിചയം                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ ലോക വൈദ്യുതി ഉപയോഗം ⚡️ - ARIMA ഉപയോഗിച്ചുള്ള ടൈം സീരീസ് ഫോറികാസ്റ്റിംഗ് |        [ടൈം സീരീസ്](7-TimeSeries/README.md)        | ARIMA ഉപയോഗിച്ചുള്ള ടൈം സീരീസ് ഫോറികാസ്റ്റിംഗ്                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ ലോക വൈദ്യുതി ഉപയോഗം ⚡️ - SVR ഉപയോഗിച്ചുള്ള ടൈം സീരീസ് ഫോറികാസ്റ്റിംഗ്  |        [ടൈം സീരീസ്](7-TimeSeries/README.md)        | Support Vector Regressor ഉപയോഗിച്ചുള്ള ടൈം സീരീസ് ഫോറികാസ്റ്റിംഗ്                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             റീഇൻഫോഴ്‌സ്‌മെന്റ് ലേണിങ്ങിലേക്ക് പരിചയം             | [റീഇൻഫോഴ്‌സ്‌മെന്റ് ലേണിംഗ്](8-Reinforcement/README.md) | Q-Learning ഉപയോഗിച്ച് റീഇൻഫോഴ്‌സ്‌മെന്റ് ലേണിങ്ങിന്റെ പരിചയം                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 പീറ്ററെ നരിയെ ഒഴിവാക്കാൻ സഹായിക്കുക! 🐺                  | [റീഇൻഫോഴ്‌സ്‌മെന്റ് ലേണിംഗ്](8-Reinforcement/README.md) | റീഇൻഫോഴ്‌സ്‌മെന്റ് ലേണിംഗ് ജിം                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            യഥാർത്ഥ ലോകത്തിലെ ML സാഹചര്യങ്ങളും പ്രയോഗങ്ങളും            |      [യഥാർത്ഥ ലോകത്തിലെ ML](9-Real-World/README.md)       | ക്ലാസിക്കൽ ML ന്റെ രസകരവും വെളിപ്പെടുത്തുന്നതിനുമായ യഥാർത്ഥ ലോക പ്രയോഗങ്ങൾ                                                               |                                             [പാഠം](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            RAI ഡാഷ്‌ബോർഡ് ഉപയോഗിച്ച് ML-ൽ മോഡൽ ഡീബഗിങ്          |      [യഥാർത്ഥ ലോകത്തിലെ ML](9-Real-World/README.md)       | Responsible AI ഡാഷ്ട്ബോര്‍ഡ് ഘടകങ്ങൾ ഉപയോഗിച്ച് മെഷീൻ ലേണിങ്ങിലെ മോഡൽ ഡീബഗിങ്                                                              |                                             [പാഠം](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [ഈ കോഴ്സിനുള്ള എല്ലാ അധിക വിഭവങ്ങളും ഞങ്ങളുടെ Microsoft Learn ശേഖരത്തിൽ കാണുക](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- [പോസ്റ്റ്-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ml/)
+
+> **ഭാഷാചുറ്റം കുറിപ്പ്**: പ്രധാനമായും Python-ൽ എഴുതപ്പെട്ടിട്ടുള്ള ഈ പാഠങ്ങൾ, പലതും R- ലും ലഭ്യമാണ്. R പാഠം പൂർത്തിയാക്കാൻ, /solution ഫോൾഡർ പരിശോധിക്കുക, അവയിൽ R പാഠങ്ങൾ കാണാം. അവയിൽ .rmd എന്നത് കാണും, ഇത് **R Markdown** ഫയലിനെയാണ് സൂചിപ്പിക്കുന്നത്. ഇത് `കോഡ് ചങ്കുകൾ` (R അല്ലെങ്കിൽ മറ്റൊരു ഭാഷ) ഉൾപ്പെടുത്തുന്ന ഒരു മാർക്ക് ഡൗൺ ഡോക്യുമെന്റിന്റെ രൂപത്തിലുള്ള ഒരു എഴുത്ത് ഫ്രെയിംവർകാണ്. ഇതിലൂടെ കോഡ്, ഔട്ട്പുട്ട്, ചിന്തകൾ എല്ലാം മാർക്ക് ഡൗണിൽ എഴുതിയിടാം. R Markdown ഫയലുകൾ PDF, HTML, Word പോലുള്ള ഫോർമാറ്റുകളിലേക്ക് പരിണമിപ്പിക്കാം.
+> **ക്വിസ് സംബന്ധിച്ച ഒരു കുറിപ്പ്**: എല്ലാ ക്വിസുകളും [Quiz App folder](../../quiz-app) ൽ ഉൾപ്പെടുത്തിയിട്ടുണ്ട്, ഓരോന്നിലും മൂന്ന് ചോദ്യങ്ങളുള്ള 52 മൊത്തം ക്വിസുകളാണ്. അവ പാഠങ്ങളിൽനിന്നും ലിങ്ക് ചെയ്തിട്ടുണ്ട്, പക്ഷേ ക്വിസ് ആപ്പ് ലോക്കലായി ഓടിക്കാം; ലോക്കലായി ഹോസ്റ്റ് ചെയ്യാനോ Azure-ലേക്ക് ഡിപ്ലോയ് ചെയ്യാനോ `quiz-app` ഫോൾഡറിൽ നൽകിയ निर्देशങ്ങൾ പാലിക്കൂ.
+
+| പാഠ നമ്പർ |                             വിഷയം                              |                   പാഠ ഗ്രൂപ്പിംഗ്                   | പഠനലക്ഷ്യങ്ങൾ                                                                                                             |                                                              ലിങ്കുചെയ്ത പാഠം                                                               |                        രചയിതാവ്                        |
+| :-------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------: |
+|    01     |                മെഷ്യൻ ലേണിങ്ങിന്റെ പരിചയം                 |      [Introduction](1-Introduction/README.md)       | മെഷ്യൻ ലേണിങ്ങിന്റെ അടിസ്ഥാന ആശയങ്ങൾ പഠിക്കുക                                                                          |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       മുഹമ്മദ്                       |
+|    02     |                മെഷ്യൻ ലേണിങ്ങിന്റെ ചരിത്രം                  |      [Introduction](1-Introduction/README.md)       | ഈ രംഗത്തെ അടിസ്ഥാന ചരിത്രം പഠിക്കുക                                                                                     |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     ജെൻ ആൻഡ് എമി                      |
+|    03     |                 നീതിമാറ്റവും മെഷ്യൻ ലേണിങ്ങും               |      [Introduction](1-Introduction/README.md)       | മെഷ്യൻ ലേണിങ് മോഡലുകൾ നിർമ്മിക്കുമ്പോൾ പരിഗണിക്കേണ്ട നീതിമാറ്റവുമായി ബന്ധപ്പെട്ട പ്രധാന തത്ത്വശാസ്ത്ര പ്രശ്നങ്ങൾ എന്തെല്ലാം ആണെന്ന് പഠിക്കുക |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        തൊമോമി                        |
+|    04     |                മെഷ്യൻ ലേണിങ്ങിന് ഉപയോഗിക്കുന്ന സാങ്കേതിക വിദ്യകൾ                |      [Introduction](1-Introduction/README.md)       | മെഷ്യൻ ലേണിങ് ഗവേഷകർ ഉപയോഗിക്കുന്ന സാങ്കേതിക വിദ്യകൾ ഏതാണ്?                                                        |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    ക്രിസ് ആൻഡ് ജെൻ                     |
+|    05     |                   റെഗ്രഷനിലെ പരിചയം                   |        [Regression](2-Regression/README.md)         | റെഗ്രഷൻ മോഡലുകൾക്ക് Python, Scikit-learn ഉപയോഗിച്ച് ആരംഭിക്കുക                                                         |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      ജെൻ • എറിക് വാൻജൗ       |
+|    06     |                നോർത്ത് അമേരിക്കൻ പമ്പ്കിൻ വിലകൾ 🎃                |        [Regression](2-Regression/README.md)         | മെഷ്യൻ ലേണിങ്ങിനായി ഡാറ്റ ക്ളീനിംഗും ദൃശ്യവത്കരണവും ചെയ്യുക                                                             |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      ജെൻ • എറിക് വാൻജൗ       |
+|    07     |                നോർത്ത് അമേരിക്കൻ പമ്പ്കിൻ വിലകൾ 🎃                |        [Regression](2-Regression/README.md)         | ലീനിയർ, പോളിനോമിയൽ റെഗ്രഷൻ മോഡലുകൾ നിർമ്മിക്കുക                                                                       |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      ജെൻ, ഡിമിട്രി • എറിക് വാൻജൗ       |
+|    08     |                നോർത്ത് അമേരിക്കൻ പമ്പ്കിൻ വിലകൾ 🎃                |        [Regression](2-Regression/README.md)         | ഒരു ലൊജിസ്റ്റിക് റെഗ്രഷൻ മോഡൽ നിർമ്മിക്കുക                                                                                  |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      ജെൻ • എറിക് വാൻജൗ       |
+|    09     |                          ഒരു വെബ് ആപ്പ് 🔌                          |           [Web App](3-Web-App/README.md)            | പരിശീലിപ്പിച്ച മോഡൽ ഉപയോഗിക്കുന്ന ഒരു വെബ് ആപ്പ് നിർമ്മിക്കുക                                                                |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         ജെൻ                          |
+|    10     |                 ക്ലാസിഫിക്കേഷനിൽ പരിചയം                 |    [Classification](4-Classification/README.md)     | ഡാറ്റ ശുദ്ധീകരിക്കുകയും പ്രോസസ്സ് ചെയ്ത് ദൃശ്യവത്കരിക്കുകയും ചെയ്യുക; ക്ലാസിഫിക്കേഷനിലേക്ക് പരിചയം                              | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | ജെൻ, കാസ്സി • എറിക് വാൻജൗ |
+|    11     |             ആസിയൻ, ഇന്ത്യൻ വിഭവങ്ങളുടെ രുചികൈകൾ 🍜             |    [Classification](4-Classification/README.md)     | ക്ലാസിഫയർമാരെ പരിചയപ്പെടുക                                                                                                | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | ജെൻ, കാസ്സി • എറിക് വാൻജൗ |
+|    12     |             ആസിയൻ, ഇന്ത്യൻ വിഭവങ്ങളുടെ രുചികൈകൾ 🍜             |    [Classification](4-Classification/README.md)     | കൂടുതൽ ക്ലാസിഫയർമാർ                                                                                                     | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | ജെൻ, കാസ്സി • എറിക് വാൻജൗ |
+|    13     |             ആസിയൻ, ഇന്ത്യൻ വിഭവങ്ങളുടെ രുചികൈകൾ 🍜             |    [Classification](4-Classification/README.md)     | നിങ്ങളുടെ മോഡൽ ഉപയോഗിച്ച് ഒരു റികമന്‍ഡർ വെബ് ആപ്പ് നിർമ്മിക്കുക                                                               |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         ജെൻ                          |
+|    14     |                   ക്ലസ്റ്ററിംഗിൽ പരിചയം                   |        [Clustering](5-Clustering/README.md)         | ഡാറ്റ ശുദ്ധീകരിച്ച് പ്രോസസ്സ് ചെയ്ത് ദൃശ്യവത്കരിക്കുക; ക്ലസ്റ്ററിംഗിലേക്ക് പരിചയം                                              |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      ജെൻ • എറിക് വാൻജൗ       |
+|    15     |              നൈജീരിയൻ മ്യൂസിക്കൽ രുചികൾ അന്വേഷിക്കുന്നത് 🎧              |        [Clustering](5-Clustering/README.md)         | K-മീൻസ് ക്ലസ്റ്ററിംഗ് രീതിയെക്കുറിച്ച് പഠിക്കുക                                                                             |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      ജെൻ • എറിക് വാൻജൗ       |
+|    16     |        നാചുറൽ ലാംഗ്വേജ് പ്രോസസ്സിങ്ങിലേക്ക് പരിചയം ☕️         |   [Natural language processing](6-NLP/README.md)    | ലളിതമായ ഒരു ബോട്ട് നിർമ്മിച്ചുകൊണ്ട് NLP അടിസ്ഥാനങ്ങൾ പഠിക്കുക                                                               |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       സ്റ്റെഫൻ                        |
+|    17     |                      പൊതുവായ NLP ടാസ്‌കുകൾ ☕️                      |   [Natural language processing](6-NLP/README.md)    | ഭാഷാശാസ്ത്ര ഘടനകളുമായി ഇടപഴകുമ്പോൾ ആവശ്യമായ പൊതുവായ ടാസ്‌കുകൾ മനസിലാക്കി നിങ്ങളുടെ NLP അറിവ് മെച്ചപ്പെടുത്തുക                |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       സ്റ്റെഫൻ                        |
+|    18     |             ഭാഷാന്തരവും സേന്റ്‌മെന്റ് അനാലിസിസും ♥️              |   [Natural language processing](6-NLP/README.md)    | ജെയിൻ ഓസ്റ്റനുമായി ഭാഷാന്തരവും സേന്റ്‌മെന്റ് അനാലിസിസും                                                                    |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       സ്റ്റെഫൻ                        |
+|    19     |                  യൂറോപ്യൻ റോമാൻറിക് ഹോട്ടലുകൾ ♥️                  |   [Natural language processing](6-NLP/README.md)    | ഹോട്ടൽ റിവ്യൂകൾ ഉപയോഗിച്ചുള്ള സേന്റ്‌മെന്റ് അനാലിസിസ് 1                                                                    |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       സ്റ്റെഫൻ                        |
+|    20     |                  യൂറോപ്യൻ റോമാൻറിക് ഹോട്ടലുകൾ ♥️                  |   [Natural language processing](6-NLP/README.md)    | ഹോട്ടൽ റിവ്യൂകൾ ഉപയോഗിച്ചുള്ള സേന്റ്‌മെന്റ് അനാലിസിസ് 2                                                                    |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       സ്റ്റെഫൻ                        |
+|    21     |            ടൈം സീരീസ് ഫോർകാസ്റ്റിങ്ങിലേക്ക് പരിചയം             |        [Time series](7-TimeSeries/README.md)        | ടൈം സീരീസ് ഫോർകാസ്റ്റിങ്ങിലേക്ക് പരിചയം                                                                                      |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      ഫ്രാൻസെസ്ക്കാ                       |
+|    22     | ⚡️ വേൾഡ് പവർ ഉപയോഗം ⚡️ - ARIMA-യോടെ ടൈം സീരീസ് ഫോർകാസ്റ്റിംഗ് |        [Time series](7-TimeSeries/README.md)        | ARIMA ഉപയോഗിച്ച് ടൈം സീരീസ് ഫോർകാസ്‌റ്റിംഗ്                                                                                 |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      ഫ്രാൻസെസ്ക്കാ                       |
+|    23     |  ⚡️ വേൾഡ് പവർ ഉപയോഗം ⚡️ - SVR-യോടെ ടൈം സീരീസ് ഫോർകാസ്റ്റിംഗ്  |        [Time series](7-TimeSeries/README.md)        | Support Vector Regressor ഉപയോഗിച്ച് ടൈം സീരീസ് ഫോർകാസ്റ്റിംഗ്                                                                |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       അനിർബാൻ                        |
+|    24     |             റയിൻഫോഴ്‌സ്‌മെന്റ് ലേണിങ്ങിലേക്ക് പരിചയം             | [Reinforcement learning](8-Reinforcement/README.md) | Q-ലേണിങ്ങുമായി റയിൻഫോഴ്‌സ്‌മെന്റ് ലേണിങ് പരിചയപ്പെടുത്തൽ                                                                  |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        ഡിമിട്രി                        |
+|    25     |                 പീറ്റർനെ അദ്ധമരിൽ നിന്നു രക്ഷിക്കൂ! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | റയിൻഫോഴ്‌സ്‌മെന്റ് ലേണിങ് ജിം                                                                                             |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        ഡിമിട്രി                        |
+| Postscript |            യഥാർത്ഥ ലോകത്തിലെ മെഷ്യൻ ലേണിങ് ഘടകങ്ങളും പ്രയോഗങ്ങളും            |      [ML in the Wild](9-Real-World/README.md)       | ക്ലാസ്സിക്കൽ മെഷ്യൻ ലേണിങ്ങിന്റെ രസകരവും വെളിച്ചം ചൊരിയുന്ന യഥാർത്ഥ ലോക പ്രയോഗങ്ങൾ                                              |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         ടീം                         |
+| Postscript |            RAI ഡാഷ്ബോർഡ് ഉപയോഗിച്ച് ML മോഡൽ ഡീബഗിംഗ്          |      [ML in the Wild](9-Real-World/README.md)       | Responsible AI ഡാഷ്ബോർഡ് ഘടകങ്ങൾ ഉപയോഗിച്ച് മെഷീൻ ലേണിങ് മോഡലുകൾ ഡീബഗ് ചെയ്യൽ                                             |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         റുത്ത് യാകുബു                       |
+
+> [ഈ കോഴ്സിന്റെ എല്ലാ അധിക വിഭവങ്ങളും നമ്മുടെ Microsoft Learn ശേഖരത്തിൽ കണ്ടെത്തുക](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## ഓഫ്‌ലൈൻ ആക്‌സസ്
 
-ഈ ഡോക്യുമെന്റേഷൻ [Docsify](https://docsify.js.org/#/) ഉപയോഗിച്ച് ഓഫ്‌ലൈൻ ആയി പ്രവർത്തിപ്പിക്കാവുന്നതാണ്. ഈ റിപ്പോസിറ്ററി ഫോർക്ക് ചെയ്യുക, നിങ്ങളുടെ ലൊക്കൽ മെഷീനിൽ [Docsify ഇൻസ്റ്റാൾ ചെയ്യുക](https://docsify.js.org/#/quickstart), തുടർന്ന് ഈ റിപ്പോയുടെ റൂട്ട് ഫോൾഡറിൽ `docsify serve` ടൈപ്പ് ചെയ്യുക. വെബ്സൈറ്റ് നിങ്ങളുടെ localhost-ൽ പോർട്ട് 3000-ൽ സർവുചെയ്യപ്പെടുന്ന būs: `localhost:3000`.
+നിങ്ങൾക്ക് [Docsify](https://docsify.js.org/#/) ഉപയോഗിച്ച് ഈ ദസ്താവേധം ഓഫ്‌ലൈൻ പ്രവർത്തിപ്പിക്കാം. ഈ റിപ്പോ ഫോർക്കുചെയ്‌ത്, നിങ്ങളുടെ ലോകൽ മെഷീനിൽ [Docsify ഇൻസ്റ്റാൾ ചെയ്യുക](https://docsify.js.org/#/quickstart), പിന്നീട് ഈ റിപ്പോയുടെ റൂട്ട് ഫോൾഡറിൽ `docsify serve` എന്ന് ടൈപ്പ് ചെയ്യുക. വെബ്സൈറ്റ് പോർട്ട് 3000-ൽ ലോക്കൽഹോസ്റ്റിൽ സേവനമാകും: `localhost:3000`.
 
-## PDF ഫയലുകൾ
+## PDFകൾ
 
-പഠന പരിപാടിയുടെ ലിങ്കുകളോടുകൂടിയ PDF [ഇവിടെ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) കണ്ടെത്തുക.
+കോഴ്സിന്റെ പിഡിഎഫ് രൂപം ലിങ്കുകളോടെ [ഇവിടെ കണ്ടെത്തൂ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
 ## 🎒 മറ്റ് കോഴ്സുകൾ 
 
-ഞങ്ങളുടെ ടീം മറ്റ് കോഴ്സുകളും നിർമ്മിക്കുന്നു! ഇവ പരിശോധിക്കുക:
+നമ്മുടെ ടീം മറ്റ് കോഴ്സുകളും ഉല്പാദിപ്പിക്കുന്നു! പരിശോധിക്കുക:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j ആരംഭക്കാർക്കുള്ളത്](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 ആരംഭക്കാർക്കുള്ളത്](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 ആരംഭക്കാർക്കുള്ളത്](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 ആരംഭക്കാർക്കുള്ളത്](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 ആരംഭക്കാർക്കുള്ളത്](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 ഏജന്റുകൾ ആരംഭക്കാർക്കുള്ളത്](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)
 
 ---
  
-### ജനറേറ്റീവ് AI പരമ്പര
-[![ജനറേറ്റീവ് AI ആരംഭക്കാർക്കുള്ളത്](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)
-[![ജനറേറ്റീവ് 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)
-[![ജനറേറ്റീവ് എഐ (ജാവ)](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)
-[![ജനറേറ്റീവ് എഐ (ജാവാസ്ക്രിപ്റ്റ್)](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 Series
+[![ആരംഭക്കാർക്കായുള്ള ജനനാത്മക 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)
+[![ജനനാത്മക 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)
+[![ജനനാത്മക 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)
+[![ജനനാത്മക 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)
 
 ---
  
 ### പ്രധാന പഠനം
-[![ആരംഭക്കാർക്കുള്ള മെഷീൻ ലേണിംഗ്](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)
-[![ആരംഭക്കാർക്കുള്ള ഡേറ്റാ സയൻസ്](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)
-[![ആരംഭക്കാർക്കുള്ള എഐ](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)
-[![ആരംഭക്കാർക്കുള്ള സൈബർസെക്യൂരിറ്റി](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)
-[![ആരംഭക്കാർക്കുള്ള വെബ് ഡെവലപ്പ്മെന്റ്](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)
-[![ആരംഭക്കാർക്കുള്ള ഐഒടി](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)
-[![ആരംഭക്കാർക്കുള്ള XR ഡെവലപ്‌മെന്റ്](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)
+[![ആരംഭക്കാർക്കായുള്ള ML](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)
+[![ആരംഭക്കാർക്കായുള്ള ഡാറ്റാ സയൻസ്](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)
+[![ആരംഭക്കാർക്കായുള്ള AI](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)
+[![ആരംഭക്കാർക്കായുള്ള സൈബർസുരക്ഷ](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)
+[![ആരംഭക്കാർക്കായുള്ള വെബ് വികസനം](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)
+[![ആരംഭക്കാർക്കായുള്ള ഐഒടി](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)
+[![ആരംഭക്കാർക്കായുള്ള XR വികസനം](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)
 
 ---
  
-### കോപൈലറ്റ് സീരീസ്
-[![AI കൂട്ടുപ്രോഗ്രാമിംഗിനുള്ള 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-ക്കായുള്ള 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 അഡ്വഞ്ചർ](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)
+### കോപൈലറ്റ് പരമ്പര
+[![AI കൂട്ടായ്മാ പ്രോഗ്രാമിംഗിനായുള്ള കോപൈലറ്റ്](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-നുള്ള കോപൈലറ്റ്](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)
+[![കോപൈലറ്റ് സാഹസം](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 -->
 
-## സഹായം
+## സഹായം ലഭിക്കുന്നതു്
 
-എഐ ആപ്പുകൾ നിർമ്മിക്കുന്നതിൽ കുടുങ്ങുക അല്ലെങ്കിൽ ഏതെങ്കിലും ചോദ്യങ്ങൾ ഉണ്ടയാൽ, MCP സംബന്ധിച്ച ചർച്ചകളിൽ മറ്റു പഠനക്കാരെയും പരിചയസമ്പന്നരുമായ ഡെവലപ്പർമാരെയും ചേർക്കുക. ചോദ്യങ്ങൾക്ക് സ്വാഗതം പറയുന്നതും അറിവുകൾ സ്വതന്ത്രമായി പങ്കുവെയ്ക്കപ്പെടുന്നതുമായൊരു പിന്തുണയുള്ള സമുദായമാണ് ഇത്.
+നിങ്ങള്‍ AI ആപ്ലിക്കേഷനുകള്‍ നിര്‍മ്മിക്കുമ്പോള്‍ തടസ്സപ്പെടുകയോ ഏതെങ്കിലും ചോദ്യം ഉണ്ടാവുകയോ ചെയ്താല്‍. MCP-യുമായി ബന്ധപ്പെട്ട അനുഭവസമ്പന്നരായ ഡെവലപ്പര്‍മാരും പഠിക്കുന്നവരുമായ കൂട്ടായ്മയില്‍ പങ്കെടുക്കുക. ചോദ്യങ്ങള്‍ക്ക് സ്വീകാര്യതയുള്ളതും അറിവ് സ്വതന്ത്രമായി പങ്കുവെക്കുന്നും ഒരു പിന്തുണയുള്ള സമൂഹം ഇത്.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-നിർമ്മിക്കുന്നതിനിടെ ഉൽപ്പന്ന അഭിപ്രായങ്ങൾ അല്ലെങ്കിൽ പിശകുകൾ ഉണ്ടെങ്കിൽ സന്ദർശിക്കുക:
+നിങ്ങള്‍ക്ക് ഉല്‍പ്പന്നത്തിന്റെ പ്രതികരണമോ നിര്‍മ്മാണ സമയത്ത് പിശകുകളോ ഉണ്ടെങ്കില്‍, സന്ദര്‍ശിക്കുക:
 
 [![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 -->
-അസ്വീകരണ കുറിപ്പ്:
-ഈ രേഖ AI അധിഷ്ഠിത പരിഭാഷാ സേവനമായ Co-op Translator (https://github.com/Azure/co-op-translator) ഉപയോഗിച്ച് വിവർത്തനം ചെയ്തതാണ്. ഞങ്ങൾ കൃത്യതയ്ക്ക് ശ്രമിച്ചുവെങ്കിലും ഓട്ടോമേറ്റഡ് വിവർത്തനങ്ങളിൽ പിശകുകൾ അല്ലെങ്കിൽ അശുദ്ധതകൾ ഉണ്ടായിരിക്കാമെന്ന് ദയവായി ശ്രദ്ധിക്കുക. ഈ രേഖയുടെ സ്വദേശ ഭാഷയിലെ യഥാർത്ഥ പ്രമാണം പ്രാമാണിക ഉറവിടമായി കണക്കാക്കണം. നിർണായക വിവരങ്ങൾക്ക് പ്രൊഫഷണൽ മനുഷ്യ പരിഭാഷ ശുപാർശ ചെയ്യുന്നു. ഈ വിവർത്തനം ഉപയോഗിച്ചതിലുണ്ടാകുന്ന ഏതെങ്കിലും തെറ്റിദ്ധാരണകൾക്കോ തെറ്റായ വ്യാഖ്യാനങ്ങൾക്കോ ഞങ്ങൾ ഉത്തരവാദികളല്ല.
+**അസാധുവാക്കൽ**:  
+ഈ പ്രമാണം AI പരിഭാഷാ സേവനം [Co-op Translator](https://github.com/Azure/co-op-translator) ഉപയോഗിച്ചു പരിഭാഷപ്പെടുത്തപ്പെട്ടതാണ്. ഞങ്ങൾ കൃത്യതയ്ക്കായി പരിശ്രമിച്ചാലും, സ്വയം പ്രവർത്തിക്കുന്ന പരിഭാഷകളിൽ പിഴവുകൾ അല്ലെങ്കിൽ തെറ്റിച്ചെപ്പുകൾ ഉണ്ടാകാമെന്ന് ദയവായി ശ്രദ്ധിക്കുക. പ്രമാണത്തിന്റെ അസൽ ഭാഷയിലാണ് പ്രാമാണിക ഉറവിടം എന്ന് കാണണം. അത്യാവശ്യമുള്ള വിവരങ്ങൾക്ക് പ്രൊഫഷണൽ മാനവ പരിഭാഷ ശിപാർശ ചെയ്യപ്പെടുന്നു. ഈ പരിഭാഷ ഉപയോഗത്തിൽ നിന്നുണ്ടാകുന്ന ഏതെങ്കിലും പൈരവലക്ഷണംകൾക്കോ വ്യക്തമായ തെറ്റിഷ്ടങ്ങൾക്കോ ഞങ്ങൾ ഉത്തരവാദികളല്ല.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/te/README.md b/translations/te/README.md
index 7c1659ca3..4f4948c26 100644
--- a/translations/te/README.md
+++ b/translations/te/README.md
@@ -1,219 +1,205 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T14:28:26+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T17:56:07+00:00",
   "source_file": "README.md",
   "language_code": "te"
 }
 -->
-[![GitHub లైసెన్స్](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![GitHub కాంట్రిబ్యూటర్స్](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![GitHub సమస్యలు](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![GitHub పుల్-రిక్వెస్ట్‌లు](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRలు స్వాగతం](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
-
-[![Microsoft Foundry డిస్కార్డ్](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
-
 ### 🌐 బహుభాషా మద్దతు
 
-#### GitHub Action ద్వారా మద్దతు (ఆటోమేటెడ్ & ఎప్పుడూ అప్-టు-డేట్)
+#### GitHub చర్య ద్వారా మద్దతు (ఆటోమేటెడ్ & ఎప్పుడూ తాజా)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[అరబిక్](../ar/README.md) | [బెంగాలీ](../bn/README.md) | [బల్గేరియన్](../bg/README.md) | [బర్మీస్ (మయన్మార్)](../my/README.md) | [చైనీస్ (సరళీకృత)](../zh/README.md) | [చైనీస్ (సాంప్రదాయ, హాంకాంగ్)](../hk/README.md) | [చైనీస్ (సాంప్రదాయ, మకావు)](../mo/README.md) | [చైనీస్ (సాంప్రదాయ, తైవాన్)](../tw/README.md) | [క్రొయేషియన్](../hr/README.md) | [చెక్](../cs/README.md) | [డానిష్](../da/README.md) | [డచ్](../nl/README.md) | [ఎస్తోనియన్](../et/README.md) | [ఫిన్నిష్](../fi/README.md) | [ఫ్రెంచ్](../fr/README.md) | [జర్మన్](../de/README.md) | [గ్రీకు](../el/README.md) | [హెబ్రూ](../he/README.md) | [హిందీ](../hi/README.md) | [హంగేరియన్](../hu/README.md) | [ఇండోనేషియన్](../id/README.md) | [ఇటాలియన్](../it/README.md) | [జపనీస్](../ja/README.md) | [కన్నడ](../kn/README.md) | [కొరియన్](../ko/README.md) | [లిథువేనియన్](../lt/README.md) | [మలయ్](../ms/README.md) | [మలయాళం](../ml/README.md) | [మరాఠీ](../mr/README.md) | [నేపాలి](../ne/README.md) | [నైజీరియన్ పిడ్జిన్](../pcm/README.md) | [నార్వెజియన్](../no/README.md) | [పర్షియన్ (ఫార్సీ)](../fa/README.md) | [పోలిష్](../pl/README.md) | [పోర్టుగీస్ (బ్రజిల్)](../br/README.md) | [పోర్టుగీస్ (పోర్టుగల్)](../pt/README.md) | [పంజాబీ (గుర్ముఖి)](../pa/README.md) | [రొమానియన్](../ro/README.md) | [రష్యన్](../ru/README.md) | [సర్బియన్ (సిరిలిక్)](../sr/README.md) | [స్లోవాక్](../sk/README.md) | [స్లోవేనియన్](../sl/README.md) | [స్పానిష్](../es/README.md) | [స్వాహిలి](../sw/README.md) | [స్వీడిష్](../sv/README.md) | [టాగలోగ్ (ఫిలిపినో)](../tl/README.md) | [తమిళ్](../ta/README.md) | [తెలుగు](./README.md) | [థాయ్](../th/README.md) | [టర్కిష్](../tr/README.md) | [ఉక్రెయిన్](../uk/README.md) | [ఉర్దూ](../ur/README.md) | [వియత్నామీస్](../vi/README.md)
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
-
-#### మా కమ్యూనిటీలో చేరండి
-
-We have a Discord learn with AI series ongoing, learn more and join us at [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.
+[అరబిక్](../ar/README.md) | [బెంగాలీ](../bn/README.md) | [బల్గేరియన్](../bg/README.md) | [బుర్మీస్ (మయన్మార్)](../my/README.md) | [చైనీస్ (సరళీకృత)](../zh/README.md) | [చైనీస్ (పారంపర్య, హాంకాంగ్)](../hk/README.md) | [చైనీస్ (పారంపర్య, మెకావు)](../mo/README.md) | [చైనీస్ (పారంపర్య, తైవాన్)](../tw/README.md) | [క్రొయేషియన్](../hr/README.md) | [చెక్](../cs/README.md) | [డానిష్](../da/README.md) | [డచ్](../nl/README.md) | [ఎస్టోనియన్](../et/README.md) | [ఫిన్నిష్](../fi/README.md) | [ఫ్రెంచ్](../fr/README.md) | [జర్మన్](../de/README.md) | [గ్రీకు](../el/README.md) | [హీబ్రూ](../he/README.md) | [హిందీ](../hi/README.md) | [హుంగేరియన్](../hu/README.md) | [ఇండోనేషియన్](../id/README.md) | [ఇటాలియన్](../it/README.md) | [జపనీస్](../ja/README.md) | [కన్నడ](../kn/README.md) | [కొరియన్](../ko/README.md) | [లిథ్వేనియన్](../lt/README.md) | [మలయ్](../ms/README.md) | [మలయాళం](../ml/README.md) | [మరాఠీ](../mr/README.md) | [నేపాలి](../ne/README.md) | [నైజీరియన్ పిడ్గిన్](../pcm/README.md) | [నార్వేజియన్](../no/README.md) | [పర్షియన్ (ఫార్సీ)](../fa/README.md) | [పోలిష్](../pl/README.md) | [పోర్చుగീസ് (బ్రెజిల్)](../br/README.md) | [పోర్చుగీస్ (పోర్చుగల్)](../pt/README.md) | [పంజాబీ (గురుముఖీ)](../pa/README.md) | [రోమానియన్](../ro/README.md) | [రష్యన్](../ru/README.md) | [సెర్బియన్ (సిరిలిక్)](../sr/README.md) | [స్లోవక్](../sk/README.md) | [స్లోవేనియన్](../sl/README.md) | [స్పానిష్](../es/README.md) | [స్వాహిలి](../sw/README.md) | [స్వీడిష్](../sv/README.md) | [తగలోగ్ (ఫిలిపినో)](../tl/README.md) | [తమిళ్](../ta/README.md) | [తెలుగు](./README.md) | [థాయ్](../th/README.md) | [టర్కిష్](../tr/README.md) | [ఉక్రేనియన్](../uk/README.md) | [ఉర్దూ](../ur/README.md) | [వియత్నామీస్](../vi/README.md)
 
-![AI తో నేర్చుకునే సిరీస్](../../translated_images/te/3.9b58fd8d6c373c20.webp)
+> **స్థానికంగా క్లోన్ చేయడం ఇష్టమైనా?**
 
-# ప్రారంభికుల కోసం మెషిన్ లెర్నింగ్ - ఒక పాఠ్యక్రమం
-
-> 🌍 ప్రపంచ సంస్కృతుల ద్వారా మెషిన్ లెర్నింగ్‌ను అన్వేషిస్తూ ప్రపంచాన్ని చుట్టి ప్రయాణించండి 🌍
-
-Microsoft లోని క్లౌడ్ అడ్వొకేట్స్ ఒక 12-వారం, 26-పాఠాల పాఠ్యక్రమాన్ని అందించడం ద్వారా ఆనందంగా ఉన్నారు, ఇది మొత్తం **మెషిన్ లెర్నింగ్** గురించి. ఈ పాఠ్యక్రమంలో, మీరు సాధారణంగా Scikit-learn లైబ్రరీని ఉపయోగించి మరియు డీప్ లెర్నింగ్‌ను తప్పించుకుంటూ, కsometimesాచుకొని "క్లాసిక్ మెషిన్ లెర్నింగ్" అని పిలవబడే విషయాన్ని తెలుసుకుంటారు, డీప్ లెర్నింగ్ గురించి మా [AI ప్రారంభుల పాఠ్యక్రమం](https://aka.ms/ai4beginners) లో వివరించబడింది. ఈ పాఠ్యాల్ని మా ['డేటా సైన్స్ ప్రారంభులకు' పాఠ్యక్రమం](https://aka.ms/ds4beginners) తో జత చేయండి కూడా.
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-ప్రపంచం చుట్టూ మా ప్రయాణంలో, మేము ఈ క్లాసిక్ సాంకేతికతలను ప్రపంచంలోని అనేక ప్రాంతాల డేటాకు వర్తింపజేస్తున్నాము. ప్రతి పాఠంలో పాఠానికి ముందు మరియు పాఠానికి తరువాత క్విజ్‌లు, పాఠాన్ని పూర్తి చేయడానికి రాసిన సూచనలు, ఒక పరిష్కారం, ఒక అసైన్మెంట్ మరియు మరిన్ని ఉన్నాయి. మా ప్రాజెక్ట్-ఆధారిత పాఠ్య విధానం మీరు నిర్మించడం ద్వారా నేర్చుకోవడానికి అనుమతిస్తుంది, కొత్త నైపుణ్యాలు 'unalకి ఉండటానికి' ఈ provedవ విధానం నిరూపితంగా ఉపయోగపడుతుంది.
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-**✍️ మా రచయితలకు హృదయపూర్వక కృతజ్ఞతలు** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+> ఈ రిపాజిటరీ 50+ భాషల అనువాదాలను కలిగి ఉండటం ద్వారా డౌన్లోడ్ పరిమాణం గణనీయంగా పెరుగుతుంది. అనువాదాలు లేకుండా క్లోన్ చేయడానికి, స్పార్స్ చెకౌట్ ఉపయోగించండి:  
+> ```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'
+> ```
+> ఇది మీరు కోర్సును పూర్తి చేసుకోవడానికి అవసరమైన అన్ని విషయాలను మరింత వేగవంతమైన డౌన్లోడ్‌తో ఇస్తుంది.
+<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**🎨 మా చిత్రరచయితలకు కూడా ధన్యవాదాలు** Tomomi Imura, Dasani Madipalli, and Jen Looper
+#### మా కమ్యూనిటీకి చేరండి
 
-**🙏 మా Microsoft Student Ambassador రచయితలు, సమీక్షకులు, మరియు కంటెంట్ సహకారులకు ప్రత్యేక కృతజ్ఞతలు**, ముఖ్యంగా Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, మరియు Snigdha Agarwal
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-**🤩 మా R పాఠాల కోసం Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, మరియు Vidushi Gupta గారికి అదనపు కృతజ్ఞతలు!**
+మేము ఒక Discord లో AI తో నేర్చుకోవడం సిరీస్ నిర్వహిస్తున్నాము, మరింత తెలుసుకోవడానికి మరియు మాతో చేరడానికి [Learn with AI Series](https://aka.ms/learnwithai/discord) వద్ద 18 - 30 సెప్టెంబర్, 2025 తేదీలలో చేరండి. మీరు GitHub Copilot ను డేటా సైన్స్ కోసం ఉపయోగించే చిట్కాలు మరియు తంత్రాలు పొందుతారు.
 
-# ప్రారంభించడం
+![Learn with AI series](../../../../translated_images/te/3.9b58fd8d6c373c20.webp)
 
-ఈ దశలను అనుసరించండి:
-1. **రిపొజిటరీని ఫార్క్ చేయండి**: ఈ పేజీ యొక్క పై-కుడి మూలంలో ఉన్న "Fork" బటన్‌పై క్లిక్ చేయండి.
-2. **రిపొజిటరీని క్లోన్ చేయండి**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+# మొదలుపెట్టు
 
-> [ఈ కోర్సుకు సంబంధించిన అన్ని అదనపు వనరులను మా Microsoft Learn సేకరణలో కనుగొనండి](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+ఈ దశలను అనుసరించండి:  
+1. **రిపాజిటరీని ఫోర్క్ చేయండి**: ఈ పేజీ యొక్క పై-కుడి మూలలో "Fork" బటన్‌పై క్లిక్ చేయండి.  
+2. **రిపాజిటరీని క్లోన్ చేయండి**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> 🔧 **సహాయం కావాలా?** ఇన్‌స్టాలేషన్, సెటప్ మరియు పాఠాలను నడుపుతున్న సమయంలో సాధారణ సమస్యల పరిష్కారాల కోసం మా [ట్రబుల్‌షూటింగ్ గైడ్](TROUBLESHOOTING.md) ను తనిఖీ చేయండి.
+> [ఈ కోర్సుకి సంబంధించిన అన్ని అదనపు వనరులను మా Microsoft Learn సేకరణలో పొందండి](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
+> 🔧 **సహాయం కావాలా?** ఇన్‌స్టాలేషన్, సెటప్ మరియు పాఠాలు నడపడంలో సాధారణ సమస్యల పరిష్కారాలకు మా [Troubleshooting Guide](TROUBLESHOOTING.md) చూడండి.
 
-**[విద్యార్థులు](https://aka.ms/student-page)**, ఈ పాఠ్యక్రమాన్ని ఉపయోగించడానికి, మొత్తం రిపోను మీ GitHub ఖాతాకు ఫార్క్ చేయండి మరియు ఒకరితో లేదా సమూహంతో స్వతంత్రంగా వ్యాయామాలను పూర్తి చేయండి:
+**[స్టూడెంట్లు](https://aka.ms/student-page)**, ఈ పాఠ్యాన్ని ఉపయోగించడానికి, మొత్తం రిపోను మీ GitHub ఖాతాకు ఫోर्क్ చేసి ప్రత్యేక్గా లేదా గ్రూప్ తో వ్యాయామాలు పూర్తి చేయండి:
 
-- పాఠశాల ముందు ఒక ప్రీ-లెక్చర్ క్విజ్‌తో ప్రారంభించండి.
-- లెక్చర్‌ను చదవండి మరియు చర్యలను పూర్తి చేయండి, ప్రతి నాలెడ్జ్ చెక్ వద్ద ఆపివేసి ఆలోచించండి.
-- పరిష్కార కోడ్‌ని నేరుగా నడిపే పనిని కాకుండా పాఠాలను అర్థం చేసుకుని ప్రాజెక్టులను సృష్టించడానికి ప్రయత్నించండి; అయితే ఆ కోడ్ ప్రతి ప్రాజెక్ట్-ఆధారిత పాఠంలోని `/solution` ఫోల్డర్లలో అందుబాటులో ఉంటుంది.
-- పాఠ శేషంలో పోస్ట్-లెక్చర్ క్విజ్ తీసుకోండి.
-- చాలెంజ్‌ను పూర్తి చేయండి.
-- అసైన్మెంట్‌ను పూర్తి చేయండి.
-- ఒక పాఠ గ్రూప్‌ను పూర్తిచేసిన తర్వాత, [చర్చ ఫోరం](https://github.com/microsoft/ML-For-Beginners/discussions) ను సందర్శించి, సరైన PAT రుబ్రిక్‌ని భర్తీచేసి "తెలివిగా నేర్చుకోండి" (learn out loud). 'PAT' అనేది మీరు మీ అభ్యాసాన్ని మరింతగా పెంపొందించుకునేందుకు భర్తీచేసే ఒక ప్రోగ్రెస్ असेస్మెంట్ టూల్ (రుబ్రిక్). మీరు ఇతర PATs పై కూడా స్పందించవచ్చు కాబట్టి మేము కలిసి నేర్చుకోవచ్చు.
+- ప్రీ-లెక్చర్ క్విజ్ తో ప్రారంభించండి.  
+- లెక్చర్ ని చదివి, కార్యకలాపాలను పూర్తి చేయండి, ప్రతి జ్ఞాన పరీక్ష వద్ద ఆగిపోవడం మరియు ఆలోచించడం.  
+- పరిష్కార కోడ్ నడపకుండా పాఠాలు అర్థం చేసుకుని ప్రాజెక్టులు సృష్టించుకోవడానికి ప్రయత్నించండి; అయినప్పటికీ ఆ కోడ్ ప్రతి ప్రాజెక్ట్-ఆధారిత పాఠ్యంలోని `/solution` ఫోల్డర్‌లో అందుబాటులో ఉంటుంది.  
+- పోస్ట్-లెక్చర్ క్విజ్ తీసుకోండి.  
+- ఛాలెంజ్ పూర్తి చేయండి.  
+- అసైన్‌మెంట్ పూర్తి చేయండి.  
+- ఒక పాఠ్య సమూహం పూర్తి చేసిన తర్వాత, [చర్చ ఫోరమ్](https://github.com/microsoft/ML-For-Beginners/discussions) సందర్శించి, సంబంధించిన PAT రుబ్రిక్‌ను పూరించి "ఆవొక్కట్లుగా నేర్చుకోండి". PAT అనేది ప్రగతి అంచనా సాధనం, ఇది మీ అభ్యాసాన్ని ముందుకు తీసుకెళ్లడానికి మీరు పూరించవలసిన రుబ్రిక్. మీరు ఇతర PATలకు ప్రతిస్పందించవచ్చు, తద్వారా మనం కలిసి నేర్చుకోగలము.
 
-> తదుపరి అధ్యయనానికి, ఈ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) మాడ్యూల్స్ మరియు లర్నింగ్ పాథ్‌లను అనుసరించడం మేము సిఫార్సు చేస్తాము.
+> ఇంకా అధ్యయనం కోసం, ఈ [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) మాడ్యూల్స్ మరియు లెర్నింగ్ పాథ్లను అనుసరించడం మేము సూచిస్తాము.
 
-**ఉపాధ్యాయులు**, ఈ పాఠ్యక్రమాన్ని ఎలా ఉపయోగించాలో మేము [కొన్ని సూచనలను చేర్చాము](for-teachers.md).
+**ఉపాధ్యాయులు**, ఈ పాఠ్యాన్ని ఎలా ఉపయోగించాలో మా [సూచనలు](for-teachers.md) అందిస్తున్నాము.
 
 ---
 
-## వీడియో అవలోకనాలు
+## వీడియో వాక్‌త్రూ
 
-కొన్ని పాఠాలు షార్ట్ ఫార్మ్ వీడియోలుగా అందుబాటులో ఉన్నాయి. మీరు ఇవన్ని పాఠాలలో inlineగా కనుగొనవచ్చు, లేదా దిగువ చిత్రం పై క్లిక్ చేయడం ద్వారా [Microsoft Developer YouTube చానల్上的 ML for Beginners ప్లేలిస్ట్](https://aka.ms/ml-beginners-videos) లో పొందవచ్చు.
+కొన్ని పాఠాలు చిన్న వీడియో రూపంలో అందుబాటులో ఉన్నాయి. మీరు ఈ వీడియోలను పాఠాల్లో నేరుగా లేదా [Microsoft Developer YouTube ఛానల్‌లో ML ఫర్ బిగినర్స్ ప్లేలిస్ట్‌లో](https://aka.ms/ml-beginners-videos) బాటన్ క్లిక్ చేసి చూడవచ్చు.
 
-[![ప్రారంభుల కోసం ML బ్యానర్](../../translated_images/te/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/te/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## టీమ్‌ను పరిచయం
+## టీమ్ ని కలుసుకోండి
 
-[![ప్రోమో వీడియో](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
+[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**జిఫ్ ద్వారా** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**గిఫ్ రూపొందించిన** [మోహిత్ జైసాల్](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 ప్రాజెక్ట్ మరియు దీన్ని సృష్టించిన వారిపై వీడియో కోసం పై చిత్రాన్ని క్లిక్ చేయండి!
+> 🎥 ప్రాజెక్ట్ మరియు దీన్ని సృష్టించిన వ్యక్తుల గురించి వీడియో కోసం పై చిత్రం క్లిక్ చేయండి!
 
 ---
 
-## పాఠ్య విధానం
-
-ఈ పాఠ్యక్రమాన్ని రూపొందించేటప్పుడు మేము రెండు పాఠ్య సూత్రాలను ఎంచుకున్నాము: ఇది హ్యాండ్స్-ఆన్ గా ఉండాలని, అంటే **ప్రాజెక్ట్-ఆధారిత** ఉండాలని మరియు ఇందులో **తరచుగా క్విజ్‌లు** ఉండాలని. అదనంగా, ఈ పాఠ్యక్రమం ఒక సాధారణ **థీమ్** కలిగి ఉండటం ద్వారా ఒకరీతి సంకలనం పొందుతుంది.
-
-కంటెంట్ ప్రాజెక్ట్‌లతో సరిసమంగా ఉంటుందనడంతో, విద్యార్థులకి ఇది మరింత ఆసక్తికరంగా మారుతుంది మరియు భావనల నిల్వ పెరుగుతుంది. గమనించదగిన విషయం, తరగతి ప్రారంభానికి ముందు ఒక తేలికపాటి క్విజ్ విద్యార్థి శ్రద్ధను ఆ విషయం నేర్చుకునే దిశగా కేంద్రీకరించడానికి సహాయపడుతుంది, మరియు తరగతి తర్వాత రెండో క్విజ్ మరింత నిల్వను నిర్ధారిస్తుంది. ఈ పాఠ్యక్రమం అనుకూలంగా, సరదాగా ఉండ도록 రూపొందించబడింది మరియు మీకు మొత్తం లేదా భాగంగా తీసుకోవడానికి సరిపోతుంది. ప్రాజెక్టులు చిన్నదిగా ప్రారంభమై 12-వారం చక్రానికి చివరికి క్రమేణా క్లిష్టంగా మారతాయి. ఈ పాఠ్యక్రమంలో మెషిన్ లెర్నింగ్ యొక్క యథార్థ ప్రపంచ అనువర్తనాలపై ఒక పోస్ట్‌స్క్రిప్ట్ కూడా ఉంది, దీనిని అదనపు క్రెడిట్ లేదా చర్చకు ఆధారంగా ఉపయోగించవచ్చు.
-
-> మా [నడవడిక నియమాలు](CODE_OF_CONDUCT.md), [సహకారం](CONTRIBUTING.md), [అనువాదం](TRANSLATIONS.md), మరియు [ట్రబుల్‌షూటింగ్](TROUBLESHOOTING.md) మార్గదర్శకాలను కనుగొనండి. మీ నిర్మాణాత్మక ఫీడ్బ్యాక్‌ను మేము స్వాగతిస్తాము!
-
-## ప్రతి పాఠంలో ఉంటాయి
-
-- ఐచ్ఛిక స్కెచ్‌నోట్
-- ఐచ్ఛిక సహాయక వీడియో
-- వీడియో వాక్‍త్రూ (కొన్ని పాఠాలు మాత్రమే)
-- [పాఠానికి ముందు వార్మప్ క్విజ్](https://ff-quizzes.netlify.app/en/ml/)
-- రాసిన పాఠం
-- ప్రాజెక్ట్-ఆధారిత పాఠాల కోసం, ప్రాజెక్ట్ ఎలా నిర్మించాలో దశల వారీ మార్గదర్శకాలు
-- నాలెడ్జ్ చెక్స్
-- ఒక చాలెంజ్
-- సహాయక పఠనం
-- అసైన్మెంట్
-- [పాఠానికి తరువాత క్విజ్](https://ff-quizzes.netlify.app/en/ml/)
-
-> **భాషల గురించి ఒక సూచన**: ఈ పాఠాలు ప్రధానంగా Python లో రాసినవి, కానీ చాలా పాఠాలు R లో కూడా అందుబాటులో ఉన్నాయి. R పాఠాన్ని పూర్తి చేయడానికి, `/solution` ఫోల్డర్‌కి వెళ్లి R పాఠాలను చూడండి. అవి `.rmd` ఎగ్జ్టెన్షన్‌ను కలిగి ఉంటాయి, ఇది `R Markdown` ఫైల్‌కు చెందినది, మరియు సాధారణంగా ఇది `code chunks` (R లేదా ఇతర భాషల యొక్క) మరియు ఒక `YAML header` (PDF వంటి అవుట్‌పుట్‌ను ఎలా ఫార్మాట్ చేయాలనే దానిని మార్గదర్శకం చేస్తుంది) ని ఒక `Markdown document` లో ఎంబెడ్ చేయడం అని నిర్వచించవచ్చు. అందువల్ల, ఇది డేటా సైన్స్ కోసం ఒక ఉదాహరణాత్మక రచనా ఫ్రేమ్‌వర్క్‌గా పనిచేస్తుంది ఎందుకంటే ఇది మీ కోడ్, దాని అవుట్‌పుట్, మరియు మీ ఆలోచనలను Markdown లో రాసుకోవడానికి అనుమతిస్తుంది. అదనంగా, R Markdown డాక్యుమెంట్లు PDF, HTML, లేదా Word వంటి అవుట్‌పుట్ ఫార్మాట్స్‌కు రేందర్ చేయబడవచ్చు.
-
-> **క్విజ్‌ల గురించి ఒక సూచన**: అన్ని క్విజ్‌లు [Quiz App ఫోల్డర్](../../quiz-app) లో ఉంచబడ్డాయి, మొత్తం 52 క్విజ్‌లు, ప్రతి ఒక్కదాంట్లో మూడు ప్రశ్నలు ఉన్నాయి. అవి పాఠాలలోని లింక్‌ల ద్వారా పొందుపరచబడ్డాయి కానీ క్విజ్ యాప్‌ను లోకల్‌గా నడపవచ్చు; లోకల్ హోస్ట్ లేదా Azure కు డిప్లాయ్ చేయడానికి `quiz-app` ఫోల్డర్‌లోని సూచనలను అనుసరించండి.
-
-| పాఠం సంఖ్య |                             విషయం                              |                   పాఠ్య సమూహం                   | అధ్యయన లక్ష్యాలు                                                                                                             |                                                              లింక్ చేయబడ్డ పాఠం                                                               |                        రచయిత                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                మెషిన్ లెర్నింగ్‌కు పరిచయం                |      [పరిచయం](1-Introduction/README.md)       | మెషిన్ లెర్నింగ్ వెనుక ఉన్న ప్రాథమిక సూత్రాలను నేర్చుకోండి                                                                                |                                             [పాఠం](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                మెషిన్ లెర్నింగ్ చరిత్ర                 |      [పరిచయం](1-Introduction/README.md)       | ఈ రంగాన్ని ఆధారపెట్టిన చరిత్రను తెలుసుకోండి                                                                                         |                                            [పాఠం](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 మెషిన్ లెర్నింగ్‌లో న్యాయత                  |      [పరిచయం](1-Introduction/README.md)       | ML మోడల్స్‌ను రూపొందించేటప్పుడు మరియు వర్తింపజేసేటప్పుడు విద్యార్థులు న్యాయత గురించి పరిశీలించవలసిన ముఖ్యమైన తత్వశాస్త్రీయ అంశాలు ఏమిటి? |                                              [పాఠం](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                మెషిన్ లెర్నింగ్ కోసం సాంకేతిక విధానాలు                 |      [పరిచయం](1-Introduction/README.md)       | ML పరిశోధకులు ML మోడల్స్‌ను తయారు చేయడానికి ఏ పద్ధతులను ఉపయోగిస్తారు?                                                                       |                                          [పాఠం](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   రెగ్రెషన్‌కు పరిచయం                   |        [రెగ్రెషన్](2-Regression/README.md)         | రెగ్రెషన్ మోడల్స్ కోసం Python మరియు Scikit-learn తో మొదలు పెట్టండి                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                ఉత్తర అమెరికా గుమ్మడి ధరలు 🎃                |        [రెగ్రెషన్](2-Regression/README.md)         | ML కోసం సిద్ధం చేయడానికి డేటాను విజువలైజ్ చేసి శుభ్రం చేయండి                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                ఉత్తర అమెరికా గుమ్మడి ధరలు 🎃                |        [రెగ్రెషన్](2-Regression/README.md)         | లీనియర్ మరియు పాలినొమియల్ రెగ్రెషన్ మోడల్స్‌ను నిర్మించండి                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                ఉత్తర అమెరికా గుమ్మడి ధరలు 🎃                |        [రెగ్రెషన్](2-Regression/README.md)         | లాజిస్టిక్ రెగ్రెషన్ మోడల్ నిర్మించండి                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          వెబ్ యాప్ 🔌                          |           [వెబ్ యాప్](3-Web-App/README.md)            | మీ శిక్షణ పొందిన మోడల్ ఉపయోగించడానికి ఒక వెబ్ యాప్ నిర్మించండి                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 వర్గీకరణకు పరిచయం                 |    [వర్గీకరణ](4-Classification/README.md)     | మీ డేటాను శుభ్రపరచండి, సిద్ధం చేయండి మరియు విజువలైజ్ చేయండి; వర్గీకరణకు పరిచయం                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             రుచికరమైన ఆసియా మరియు భారతీయ వంటకాలు 🍜             |    [వర్గీకరణ](4-Classification/README.md)     | వర్గీకర్తల పరిచయం                                                                                                     | [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       |             రుచికరమైన ఆసియా మరియు భారతీయ వంటకాలు 🍜             |    [వర్గీకరణ](4-Classification/README.md)     | ఇంకా వర్గీకర్తలు                                                                                                                | [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       |             రుచికరమైన ఆసియా మరియు భారతీయ వంటకాలు 🍜             |    [వర్గీకరణ](4-Classification/README.md)     | మీ మోడల్‌ను ఉపయోగించి ఒక సిఫార్సు చేసే వెబ్ యాప్ నిర్మించండి                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   క్లస్టరింగ్‌కు పరిచయం                   |        [క్లస్టరింగ్](5-Clustering/README.md)         | మీ డేటాను శుభ్రపరచండి, సిద్ధం చేయండి మరియు విజువలైజ్ చేయండి; క్లస్టరింగ్‌కు పరిచయం                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              నైజీరియా సంగీత రుచుల అన్వేషణ 🎧              |        [క్లస్టరింగ్](5-Clustering/README.md)         | K-Means క్లస్టరింగ్ పద్ధతిని అన్వేషించండి                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్ (NLP)కు పరిచయం ☕️         |   [నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్](6-NLP/README.md)    | సాదా బాట్ నిర్మిస్తూ NLP గురించి ఆధారభూతాలను నేర్చుకోండి                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      సాధారణ NLP పనులు ☕️                      |   [నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్](6-NLP/README.md)    | భాషా నిర్మాణాలతో పని చేయడంలో అవసరమయ్యే సాధారణ పనులను అర్థం చేసుకుని మీ NLP జ్ఞానాన్ని లోతుగా చేయండి                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             అనువాదం మరియు భావన విశ్లేషణ ♥️              |   [నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్](6-NLP/README.md)    | జేన్ ఆస్టిన్ రచనలతో అనువాదం మరియు భావన విశ్లేషణ                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  యూరప్ రొమాంటిక్ హోటల్స్ ♥️                  |   [నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్](6-NLP/README.md)    | హోటల్ సమీక్షలతో భావన విశ్లేషణ 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  యూరప్ రొమాంటిక్ హోటల్స్ ♥️                  |   [నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్](6-NLP/README.md)    | హోటల్ సమీక్షలతో భావన విశ్లేషణ 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            టైమ్ సిరీస్ ఫోరకాస్టింగ్‌కు పరిచయం             |        [టైమ్ సిరీస్](7-TimeSeries/README.md)        | టైమ్ సిరీస్ ఫోరకాస్టింగ్‌కు పరిచయం                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ ప్రపంచ విద్యుత్ వినియోగం ⚡️ - ARIMA తో టైమ్ సిరీస్ ఫోరకాస్టింగ్ |        [టైమ్ సిరీస్](7-TimeSeries/README.md)        | ARIMA తో టైమ్ సిరీస్ ఫోరకాస్టింగ్                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ ప్రపంచ విద్యుత్ వినియోగం ⚡️ - SVRతో టైమ్ సిరీస్ ఫోరకాస్టింగ్  |        [టైమ్ సిరీస్](7-TimeSeries/README.md)        | Support Vector Regressorతో టైమ్ సిరీస్ ఫోరకాస్టింగ్                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             రీయిన్ఫోర్స్‌మెంట్ లెర్నింగ్‌కు పరిచయం             | [రీఇన్ఫోర్స్‌మెంట్ లెర్నింగ్](8-Reinforcement/README.md) | Q-Learning తో రీయిన్ఫోర్స్‌మెంట్ లెర్నింగ్ పరిచయం                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 పీటర్‌ను నక్క నుంచి తప్పించండి! 🐺                  | [రీఇన్ఫోర్స్‌మెంట్ లెర్నింగ్](8-Reinforcement/README.md) | రీయిన్ఫోర్స్‌మెంట్ లెర్నింగ్ జిమ్                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            వాస్తవ ప్రపంచ ML పరిస్ధితులు మరియు అనువర్తనాలు            |      [ML in the Wild](9-Real-World/README.md)       | శాస్త్రీయ ML యొక్క ఆసక్తికరమైన మరియు వెలికితీయే వాస్తవ ప్రపంచ అనువర్తనాలు                                                               |                                             [పాఠం](9-Real-World/1-Applications/README.md)                                              |                         జట్టు                         |
-|  Postscript   |            RAI డ్యాష్‌బోర్డ్ ఉపయోగించి MLలో మోడల్ డీబగ్గింగ్          |      [ML in the Wild](9-Real-World/README.md)       | Responsible AI డ్యాష్‌బోర్డ్ భాగాలు ఉపయోగించి మెషిన్ లెర్నింగ్‌లో మోడల్ డీబగ్గింగ్                                                              |                                             [పాఠం](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [ఈ కోర్సు కోసం ఉన్న అన్ని అదనపు వనరులను మా Microsoft Learn సేకరణలో కనుగొనండి](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+## పద్ధతి
+
+మేము ఈ పాఠ్యాన్ని రూపొందించేటప్పుడు రెండు పద్ధతిగత సూత్రాలను ఎంచుకున్నాము: ఇది **ప్రాజెక్ట్ ఆధారితంగా** వుండాలని మరియు **తరచూ క్విజ్‌లు** ఉండాలని. అదనంగా, ఈ పాఠ్యానికి ఒక సాధారణ **థీమ్** ను కల్గించడం జరిగింది, ఇది ఒకతని కలిసిన భావాన్ని ఇస్తుంది.
+
+విషయాలు ప్రాజెక్టులతో సరిపడే విధంగా ఉండాలనే దృష్టితో, ఇది విద్యార్థులకు మరింత ఆసక్తికరంగా చేస్తుంది మరియు ఆలోచనలు నిలుపుకోవడాన్ని పెంచుతుంది. తరగతికి ముందు తక్కువ-ప్రయత్న క్విజ్ విద్యార్థికి విషయం నేర్చుకోవడానికిబయట ఉద్దేశ్యాన్ని ఏర్పరుస్తుంది, తరగతి తర్వాత రెండవ క్విజ్ మరింత నిలుపుదలకి దోహదపడుతుంది. ఈ పాఠ్యం సరళమైనది మరియు సరదాగా ఉండేలా డిజైన్ చేయబడింది, దీన్ని మొత్తం గా లేదా భాగస్వామ్యంగా తీసుకోవచ్చు. ప్రాజెక్టులు చిన్నదిగా మొదలౌతాయి మరియు 12 వారం చక్రం చివరిలో క్రమంగా క్లిష్టత పెరుగుతుంది. ఇందులో ML యొక్క నిజజీవిత అనువర్తనాలపై ఒక పోస్ట్‌స్క్రిప్ట్ కూడా ఉంది, ఇది అదనపు క్రెడిట్ గా ఉపయోగించుకోవచ్చు లేదా చర్చకు ఆధారంగా ఉపయోగపడుతుంది.
+
+> మా [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), [Translation](TRANSLATIONS.md), మరియు [Troubleshooting](TROUBLESHOOTING.md) మార్గదర్శకాలు చూడండి. మీ గృహపాలనాత్మక అభిప్రాయాలను స్వాగతిస్తున్నాము!
+
+## ప్రతి పాఠం లో ఉంటుంది
+
+- ఐచ్ఛిక స్కెచ్నోట్  
+- ఐచ్ఛిక సప్లిమెంటల్ వీడియో  
+- వీడియో వాక్‌త్రూ (కొంత పాఠాలు మాత్రమే)  
+- [పూర్వ-లెక్చర్ వారం-అప్ క్విజ్](https://ff-quizzes.netlify.app/en/ml/)  
+- వ్రాసిన పాఠం  
+- ప్రాజెక్ట్ ఆధారిత పాఠాలకు, ప్రాజెక్ట్ నిర్మాణానికి స్తోమత దశల వారీ మార్గదర్శకాలు  
+- జ్ఞాన పరీక్షలు  
+- ఒక ఛాలెంజ్  
+- సప్లిమెంటల్ చదవడం  
+- అసైన్‌మెంట్  
+- [పోస్ట్-లెక్చర్ క్విజ్](https://ff-quizzes.netlify.app/en/ml/)
+
+> **భాషల గురించి ఒక గమనిక**: ఈ పాఠాలు ప్రధానంగా Python లో వ్రాయబడ్డాయి, కానీ చాలా R లో కూడా అందుబాటులో ఉన్నాయి. R పాఠం పూర్తి చేయడానికి, `/solution` ఫోల్డర్‌కి పోయి R పాఠాలని వెతకండి. వీటిలో .rmd ఎక్స్‌టెన్షన్ ఉంటుంది, ఇది ఒక **R Markdown** ఫైల్‌ను సూచిస్తుంది, ఇది `code chunks` (R లేదా ఇతర భాషల) మరియు `YAML header` (PDF వంటి అవుట్పుట్లను ఫార్మాట్ చేయడానికి మార్గనిర్దేశకం)ని ఒక `Markdown డాక్యుమెంట్` లోకి లోపించడమే. అందుకే, ఇది డేటా సైన్స్ కోసం ఒక అద్భుతమైన రచనా రూపకల్పన, ఎందుకంటే మీరు మీ కోడ్, దాని అవుట్పుట్, మరియు మీ ఆలోచనలను Markdown లో వ్రాయడానికి వీలుగా కలిపేలా ఉంటుంది. ఇలావుండగా, R Markdown డాక్యుమెంట్లను PDF, HTML, లేదా Word వంటి అవుట్పుట్ ఫార్మాట్లుగా మార్చవచ్చు.
+> **క్విజ్‌ల గురించి ఒక నోట్**: మొత్తం 52 క్విజ్‌లు ఉన్నాయి, వీటిలో ప్రతి క్విజ్ మూడు ప్రశ్నలు ఉన్నాయి, అవి [క్విజ్ యాప్ ఫోల్డర్](../../quiz-app)లో ఉన్నాయి. వీటిని పాఠాల నుంచి లింక్ చేయబడినవి కానీ క్విజ్ యాప్‌ను మీరు స్థానికంగా కూడా నడపవచ్చు; `quiz-app` ఫోల్డర్‌లో పొందుపరిచిన సూచనలను అనుసరించి స్థానికంగా హోస్ట్ చేయడం లేదా Azureకి డిప్లాయ్ చేయండి.
+
+| పాఠ సంఖ్య |                             విషయం                              |                   పాఠ సమూహం                   | నేర్చుకునే లక్ష్యాలు                                                                                                             |                                                              లింక్ చేయబడిన పాఠం                                                               |                        రచయిత                        |
+| :-------: | :------------------------------------------------------------: | :---------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|    01     |                మెషీన్ లెర్నింగ్ పరిచయం                |      [Introduction](1-Introduction/README.md)       | మెషీన్ లెర్నింగ్ నSTITUTEల ప్రాథమిక భావాలను నేర్చుకోండి                                                                                |                                             [Lesson](1-Introduction/1-intro-to-ML/README.md)                                             |                       ముహమ్మద్                       |
+|    02     |                మెషీన్ లెర్నింగ్ చరిత్ర                 |      [Introduction](1-Introduction/README.md)       | ఈ విభాగం ఆధారంగా చరిత్ర నేర్చుకోండి                                                                                         |                                            [Lesson](1-Introduction/2-history-of-ML/README.md)                                            |                     జెన్ మరియు అమి                      |
+|    03     |                 న్యాయం మరియు మెషీన్ లెర్నింగ్                  |      [Introduction](1-Introduction/README.md)       | న్యాయం విషయంలో ముఖ్యమైన తత్వసంబంధ సమస్యలు ఏమిటి మరియు విద్యార్థులు ML మోడళ్లను నిర్మించేటప్పుడు వాటిని ఎలా పరిగణించాలి? |                                              [Lesson](1-Introduction/3-fairness/README.md)                                               |                        టోమోమి                        |
+|    04     |                మెషీన్ లెర్నింగ్ సాంకేతిక పద్ధతులు                 |      [Introduction](1-Introduction/README.md)       | మెషీన్ లెర్నింగ్ శోధకులు ML మోడళ్లను తయారు చేయడానికి ఉపయోగించే సాంకేతిక పద్ధతులు ఏమిటి?                                                                       |                                          [Lesson](1-Introduction/4-techniques-of-ML/README.md)                                           |                    క్రిస్ మరియు జెన్                     |
+|    05     |                   రిగ్రెషన్ పరిచయం                   |        [Regression](2-Regression/README.md)         | రిగ్రెషన్ మోడళ్ల కోసం Python మరియు Scikit-learn తో ప్రారంభించండి                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      జెన్ • ఎరిక్ వాంజావ్       |
+|    06     |                ఉత్తర అమెరికన్ పంప్కిన్ ధరలు 🎃                |        [Regression](2-Regression/README.md)         | MLకి సిద్ధం కావడానికి డేటాను విజువలైజ్ చేసి శుభ్రపరచండి                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      జెన్ • ఎరిక్ వాంజావ్       |
+|    07     |                ఉత్తర అమెరికన్ పంప్కిన్ ధరలు 🎃                |        [Regression](2-Regression/README.md)         | లీనియర్ మరియు పాలినామియల్ రిగ్రెషన్ మోడళ్లను తయారు చేయండి                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      జెన్ మరియు డ్మిత్రి • ఎరిక్ వాంజావ్       |
+|    08     |                ఉత్తర అమెరికన్ పంప్కిన్ ధరలు 🎃                |        [Regression](2-Regression/README.md)         | లాజిస్టిక్ రిగ్రెషన్ మోడల్ నిర్మించండి                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      జెన్ • ఎరిక్ వాంజావ్       |
+|    09     |                          ఒక వెబ్ యాప్ 🔌                          |           [Web App](3-Web-App/README.md)            | మీ శిక్షణ పొందిన మోడల్ ఉపయోగించేందుకు వెబ్ యాప్‌ను సృష్టించండి                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         జెన్                          |
+|    10     |                 వర్గీకరణ పరిచయం                 |    [Classification](4-Classification/README.md)     | మీ డేటాను శుభ్రపరచండి, సిద్ధం చేయండి, మరియు దృశ్యీకరించండి; వర్గీకరణకు పరిచయము                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | జెన్ మరియు కాస్సీ • ఎరిక్ వాంజావ్ |
+|    11     |             రుచికరమైన ఆసియ‌న్ మరియు భారతీయ వంటకాలు 🍜             |    [Classification](4-Classification/README.md)     | వర్గీకరించే విధానాలకు పరిచయం                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | జెన్ మరియు కాస్సీ • ఎరిక్ వాంజావ్ |
+|    12     |             రుచికరమైన ఆసియ‌న్ మరియు భారతీయ వంటకాలు 🍜             |    [Classification](4-Classification/README.md)     | మరిన్ని వర్గీకరించే విధానాలు                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | జెన్ మరియు కాస్సీ • ఎరిక్ వాంజావ్ |
+|    13     |             రుచికరమైన ఆసియ‌న్ మరియు భారతీయ వంటకాలు 🍜             |    [Classification](4-Classification/README.md)     | మీ మోడల్ ఉపయోగించి ఒక సిఫారసుల వెబ్ యాప్‌ను తయారు చేయండి                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         జెన్                          |
+|    14     |                   క్లోస్టరింగ్ పరిచయం                   |        [Clustering](5-Clustering/README.md)         | మీ డేటాను శుభ్రపరచండి, సిద్ధం చేయండి, మరియు దృశ్యీకరించండి; క్లోస్టరింగ్‌కు పరిచయము                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      జెన్ • ఎరిక్ వాంజావ్       |
+|    15     |              నైజీరియన్ సంగీత రుచులను అన్వేషించడం 🎧              |        [Clustering](5-Clustering/README.md)         | K-Means క్లోస్టరింగ్ పద్ధతిని అన్వేషించండి                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      జెన్ • ఎరిక్ వాంజావ్       |
+|    16     |        సహజ భాషా ప్రాసెసింగ్ పరిచయం ☕️         |   [Natural language processing](6-NLP/README.md)    | ఒక సులభ బోట్ నిర్మించి NLP ప్రాథమికాలను నేర్చుకోండి                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       స్తీఫెన్                        |
+|    17     |                      సాధారణ NLP పనులు ☕️                      |   [Natural language processing](6-NLP/README.md)    | భాషా నిర్మాణాలతో పని చేసే సమయంలో అవసరమైన సాధారణ పనులను అర్థం చేసుకొని మీ NLP జ్ఞానం పెంచుకోండి                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       స్తీఫెన్                        |
+|    18     |             అనువాదం మరియు భావ వ్యక్తీకరణ విశ్లేషణ ♥️              |   [Natural language processing](6-NLP/README.md)    | జేన్ ఆస్టెన్‌తో అనువాదం మరియు భావ వ్యక్తీకరణ విశ్లేషణ                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       స్తీఫెన్                        |
+|    19     |                  యూరోప్ రొమాంటిక్ హోటళ్లు ♥️                  |   [Natural language processing](6-NLP/README.md)    | హోటల్ సమీక్షలతో భావ వ్యక్తీకరణ విశ్లేషణ 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       స్తీఫెన్                        |
+|    20     |                  యూరోప్ రొమాంటిక్ హోటళ్లు ♥️                  |   [Natural language processing](6-NLP/README.md)    | హోటల్ సమీక్షలతో భావ వ్యక్తీకరణ విశ్లేషణ 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       స్తీఫెన్                        |
+|    21     |            టైమ్ సిరీస్ ఫోర్కాస్టింగ్ పరిచయం             |        [Time series](7-TimeSeries/README.md)        | టైమ్ సిరీస్ ఫోర్కాస్టింగ్‌కు పరిచయం                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      ఫ్రాన్సెస్కా                       |
+|    22     | ⚡️ ప్రపంచ శక్తి వినియోగం ⚡️ - ARIMAతో టైమ్ సిరీస్ ఫోర్కాస్టింగ్ |        [Time series](7-TimeSeries/README.md)        | ARIMAతో టైమ్ సిరీస్ ఫోర్కాస్టింగ్                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      ఫ్రాన్సెస్కా                       |
+|    23     |  ⚡️ ప్రపంచ శక్తి వినియోగం ⚡️ - SVRతో టైమ్ సిరీస్ ఫోర్కాస్టింగ్  |        [Time series](7-TimeSeries/README.md)        | సపోర్ట్ వెక్టర్ రిగ్రెషన్‌తో టైమ్ సిరీస్ ఫోర్కాస్టింగ్                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       అనిర్బాన్                        |
+|    24     |             రీన్ఫోర్స్‌మెంట్ లెర్నింగ్ పరిచయం             | [Reinforcement learning](8-Reinforcement/README.md) | Q-లెర్నింగ్‌తో రీన్ఫోర్స్‌మెంట్ లెర్నింగ్ పరిచయం                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        డ్మిత్రి                        |
+|    25     |                 పీటర్‌ని నరుడి నుండి దూరంుంచండి! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | రీన్ఫోర్స్‌మెంట్ లెర్నింగ్ జిమ్                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        డ్మిత్రి                        |
+|  Postscript   |            నిజజాతి ML తరచులు మరియు అనువర్తనాలు            |      [ML in the Wild](9-Real-World/README.md)       | శాస్త్రీయ ML యొక్క ఆసక్తికరమైన మరియు వెల్లడించే నిజజాతి అనువర్తనాలు                                                               |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         బృందం                         |
+|  Postscript   |            RAI డ్యాష్‌బోర్డ్ ఉపయోగించి MLలో మోడల్ డీబగ్‌ చేయడం          |      [ML in the Wild](9-Real-World/README.md)       | రిస్పాన్సిబుల్ AI డ్యాష్‌బోర్డ్ భాగాలుతో మెషీన్ లెర్నింగ్‌లో మోడల్ డీబగ్‌ చేయడం                                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         రుత్ యాకుబు                       |
+
+> [ఈ కోర్సు కోసం మా Microsoft Learn కలెక్షన్‌లో అన్ని అదనపు వనరులను కనుగొనండి](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## ఆఫ్‌లైన్ యాక్సెస్
 
-మీరు ఈ డాక్యుమెంటేషన్‌ను ఆఫ్‌లైన్‌లో నడపడానికి [Docsify](https://docsify.js.org/#/) ఉపయోగించవచ్చు. ఈ రెపోను ఫోర్క్ చేయండి, మీ లోకల్ మెషీన్‌లో [Docsify](https://docsify.js.org/#/quickstart) ను ఇన్‌స్టాల్ చేయండి, ఆ తర్వాత ఈ రెపో యొక్క రూట్ ఫోల్డర్లో `docsify serve` టైప్ చేయండి. వెబ్‌సైట్ మీ లోకల్‌హోస్ట్‌లో పోర్ట్ 3000పై సర్వ్ చేయబడుతుంది: `localhost:3000`.
+మీరు ఈ డాక్యుమెంటేషన్‌ను ఆఫ్‌లైన్‌లో నడపడానికి [Docsify](https://docsify.js.org/#/) ఉపయోగించవచ్చు. ఈ రిపోను fork చేయండి, మీ స్థానిక మెషిన్‌పై [Docsifyను ఇన్‌స్టాల్](https://docsify.js.org/#/quickstart) చేసుకోండి, మరియు ఆ తర్వాత ఈ రిపో root ఫోల్డర్‌లో `docsify serve` టైప్ చేయండి. వెబ్‌సైట్ మీ స్థానికహోస్ట్‌లో పోర్ట్ 3000పై అందుబాటులో ఉంటుంది: `localhost:3000`.
 
-## PDFలు
+## PDF లు
 
-పాఠ్యక్రమం యొక్క లింకులతో కూడిన pdf‌ను [ఇక్కడ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) కనుగొనండి。
+లింక్‌లతో కూడిన విద్యా కార్యక్రమం PDFను [ఇక్కడ](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf) కనుగొనండి.
 
 
 ## 🎒 ఇతర కోర్సులు 
 
-మా బృందం ఇతర కోర్సులు కూడా రూపొందిస్తుంది! చూడండి:
+మా బృందం ఇతర కోర్సులను కూడా తయారు చేస్తోంది! చూడండి:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j ప్రారంభికులకు](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 ప్రారంభికులు](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 ప్రారంభికులకు](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 ప్రారంభికులకు](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 ప్రారంభికులకు](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 ఏజెంట్స్ ప్రారంభికులకు](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)
 
 ---
  
-### జనరేటివ్ AI సిరీస్
-[![జనరేటివ్ AI ప్రారంభికులకు](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 Series
+[![ఆరంభకరులకు జనరేటివ్ 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)
 [![జనరేటివ్ 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)
-[![సృజనాత్మక 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)
-[![సృజనాత్మక 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)
+[![జనరేటివ్ AI (జావా)](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)
+[![జనరేటివ్ AI (జావాస్క్రిప్ట్)](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)
 
 ---
  
-### ప్రధాన అభ్యాసాలు
-[![ప్రారంభులకు ML](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)
-[![ప్రారంభులకు డేటా సైన్స్](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)
-[![ప్రారంభులకు AI](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)
-[![ప్రారంభులకు సైబర్‌సెక్యూరిటీ](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)
-[![ప్రారంభులకు వెబ్ డెవలప్‌మెంట్](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)
-[![ప్రారంభులకు IoT](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)
-[![ప్రారంభులకు XR అభివృద్ధి](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)
+### ప్రాథమిక అభ్యాసం
+[![ప్రాథమికులకు ML](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)
+[![ప్రాథమికులకు డేటా సైన్స్](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)
+[![ప్రాథమికులకు AI](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)
+[![ప్రాథమికులకు సైబర్‌సెక్యూరిటీ](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)
+[![ప్రాథమికులకు వెబ్ డెవలప్‌మెంట్](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)
+[![ప్రాథమికులకు IoT](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)
+[![ప్రాథమికులకు XR అభివృద్ధి](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 సిరీస్
-[![AI జత ప్రోగ్రామింగ్ కోసం 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 కోసం 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 అడ్వెంచర్](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)
+### కోపైలట్ సిరీస్
+[![AI పెაუდెడ్ ప్రోగ్రామింగ్ కోసం కోపైలట్](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 కోసం కోపైలట్](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)
+[![కోపైలట్ అడ్వెంచర్](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 -->
 
-## సహాయం
+## సహాయం పొందడం
 
-మీరు AI యాప్స్ నిర్మిస్తున్నప్పుడు చిక్కుకుపోతే లేదా మీకు ఏదైనా ప్రశ్నలు ఉంటే. MCP గురించి చర్చలలో ఇతర అభ్యాసకులు మరియు అనుభవజ్ఞులైన డెవలపర్లతో చేరండి. ఇది ప్రశ్నలకు స్వాగతం చెప్పే మరియు జ్ఞానాన్ని స్వేచ্ছగా పంచుకునే మద్దతు ఇచ్చే కమ్యూనిటీ.
+మీరు AI అనువర్తనాలు నిర్మించడం గురించి ఎక్కడైనా ఇబ్బంది పడితే లేదా ఏవైనా ప్రశ్నలు ఉంటే, ఇతర అభ్యర్థులు మరియు అనుభవజ్ఞులైన అభివృద్ధికర్తలతో MCP గురించి చర్చల్లో చేరండి. ఇది ఒక మద్దతుగా ఉన్న సమాజం, అక్కడ ప్రశ్నలు స్వాగతించబడతాయి మరియు జ్ఞానం సాదాగా పంచుకుంటారు.
 
-[![Microsoft Foundry డిస్కార్డ్](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)
 
-నిర్మిస్తున్నప్పుడు ఉత్పత్తి ఫీడ్‌బ్యాక్ లేదా లోపాలు ఉంటే సందర్శించండి:
+ఉత్పత్తి అభిప్రాయం ఇవ్వడానికైనా లేదా నిర్మాణంలో లోపాలు గుర్తించడానికైనా ఈ లింక్ ద్వారా వెళ్లండి:
 
-[![Microsoft Foundry డెవలపర్ ఫోరమ్](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 -->
-జవాబుదారీ మినహాయింపు:
-ఈ పత్రాన్ని AI అనువాద సేవ [Co-op Translator](https://github.com/Azure/co-op-translator) ఉపయోగించి అనువదించబడింది. మేము ఖచ్చితత్వాన్ని లక్ష్యంగా పెట్టుకున్నప్పటికీ, స్వయంచాలక అనువాదాలలో తప్పులు లేదా లోపాలు ఉండవచ్చని దయచేసి గమనించండి. మూల భాషలోని అసలు పత్రాన్ని అధికారిక మూలంగా పరిగణించాలి. కీలకమైన సమాచారానికి వృత్తిపరమైన మానవ అనువాదాన్ని సూచిస్తాము. ఈ అనువాదాన్ని ఉపయోగించడంలోనుంచి ఏర్పడిన ఏవైనా అపార్థాలు లేదా తప్పుగా అర్థమెంచుకున్నట్లయితే అందుకు మేము బాధ్యత వహించము.
+**అస్పష్టత**:
+ఈ పత్రాన్ని AI అనువాద సేవ అయిన [Co-op Translator](https://github.com/Azure/co-op-translator) ఉపయోగించి అనువదించబడింది. మనం ఖచ్చితత్వానికి ప్రయత్నించినప్పటికీ, ఆటోమేటెడ్ అనువాదాలలో తప్పుల లేదా అసమంజసత్వాల ఉండే అవకాశం ఉన్నదని దయచేసి గమనించండి. మూల భాషలో ఉన్న الأصلي పత్రాన్ని అధికారిక మూలంగా పరిగణించాలి. కీలక సమాచారానికి, వృత్తిపరమైన మానవ అనువాదాన్ని సూచిస్తాము. ఈ అనువాదం వాడకంలో ఉన్న ఏవైనా అపార్థాలు లేదా తప్పుదారితీసే భావనలకు మేము బాధ్యులు కం.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
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