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diff --git a/translations/ar/1-Introduction/README.md b/translations/ar/1-Introduction/README.md
index 6c4fe8bda..77df00071 100644
--- a/translations/ar/1-Introduction/README.md
+++ b/translations/ar/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في هذا القسم من المنهج الدراسي، ستتعرف على المفاهيم الأساسية التي تشكل مجال تعلم الآلة، ما هو، وتتعرف على تاريخه والتقنيات التي يستخدمها الباحثون للعمل معه. دعونا نستكشف هذا العالم الجديد من تعلم الآلة معًا!
 
-![globe](../../../translated_images/ar/globe.59f26379ceb40428.jpg)
+![globe](../../../translated_images/ar/globe.59f26379ceb40428.webp)
 > صورة بواسطة <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> على <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### الدروس
diff --git a/translations/ar/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/ar/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index 05848f69d..0d7786fb5 100644
--- a/translations/ar/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/ar/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -50,7 +50,7 @@
     "   width=\"630\"/>\n",
     "   <figcaption>عمل فني بواسطة @allison_horst</figcaption>\n",
     "\n",
-    "<!--![عمل فني بواسطة \\@allison_horst](../../../../../../translated_images/ar/encouRage.e75d5fe0367fb913.jpg)<br>عمل فني بواسطة @allison_horst-->\n"
+    "<!--![عمل فني بواسطة \\@allison_horst](../../../../../../translated_images/ar/encouRage.e75d5fe0367fb913.webp)<br>عمل فني بواسطة @allison_horst-->\n"
    ],
    "metadata": {
     "id": "LWNNzfqd6feZ"
diff --git a/translations/ar/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/ar/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index b388800e0..62bee5785 100644
--- a/translations/ar/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/ar/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -227,7 +227,7 @@
     "   <figcaption>عمل فني بواسطة @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![عمل فني بواسطة \\@allison_horst](../../../../../../translated_images/ar/dplyr_wrangling.f5f99c64fd4580f1.png)<br/>عمل فني بواسطة \\@allison_horst-->\n"
+    "<!--![عمل فني بواسطة \\@allison_horst](../../../../../../translated_images/ar/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>عمل فني بواسطة \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "o4jLY5-VZO2C"
@@ -530,7 +530,7 @@
     "   <figcaption>رسم توضيحي من إعداد داساني ماديبالي</figcaption>\n",
     "\n",
     "\n",
-    "<!--![رسم توضيحي من إعداد داساني ماديبالي](../../../../../../translated_images/ar/data-visualization.54e56dded7c1a804.png){width=\"600\"}-->\n",
+    "<!--![رسم توضيحي من إعداد داساني ماديبالي](../../../../../../translated_images/ar/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "هناك مقولة *حكيمة* تقول:\n",
     "\n",
diff --git a/translations/ar/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/ar/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 9ed380598..b82ecfb9e 100644
--- a/translations/ar/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/ar/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -565,7 +565,7 @@
     "   <figcaption>رسم توضيحي بواسطة داساني ماديبالي</figcaption>\n",
     "\n",
     "\n",
-    "<!--![رسم توضيحي بواسطة داساني ماديبالي](../../../../../../translated_images/ar/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![رسم توضيحي بواسطة داساني ماديبالي](../../../../../../translated_images/ar/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "YqXjLuWavNxW"
@@ -806,7 +806,7 @@
     "   <figcaption>رسم توضيحي من إعداد داساني ماديبالي</figcaption>\n",
     "\n",
     "\n",
-    "<!--![رسم توضيحي من إعداد داساني ماديبالي](../../../../../../translated_images/ar/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![رسم توضيحي من إعداد داساني ماديبالي](../../../../../../translated_images/ar/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "HOCqJXLTwtWI"
diff --git a/translations/ar/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/ar/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index 1013ec3f6..587ec6d24 100644
--- a/translations/ar/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/ar/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## بناء نموذج الانحدار اللوجستي - الدرس الرابع\n",
     "\n",
-    "![مخطط معلوماتي عن الانحدار اللوجستي مقابل الانحدار الخطي](../../../../../../translated_images/ar/linear-vs-logistic.ba180bf95e7ee667.png)\n",
+    "![مخطط معلوماتي عن الانحدار اللوجستي مقابل الانحدار الخطي](../../../../../../translated_images/ar/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[اختبار ما قبل المحاضرة](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "الانحدار اللوجستي لا يقدم نفس الميزات التي يقدمها الانحدار الخطي. الأول يقدم توقعًا حول `فئة ثنائية` (\"برتقالي أو ليس برتقالي\")، بينما الثاني قادر على توقع `قيم مستمرة`، على سبيل المثال، بناءً على مصدر القرع ووقت الحصاد، *كم ستزداد قيمته*.\n",
     "\n",
-    "![رسم توضيحي بواسطة Dasani Madipalli](../../../../../../translated_images/ar/pumpkin-classifier.562771f104ad5436.png)\n",
+    "![رسم توضيحي بواسطة Dasani Madipalli](../../../../../../translated_images/ar/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### تصنيفات أخرى\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **رتبي**، والذي يتضمن فئات مرتبة، وهو مفيد إذا أردنا ترتيب النتائج بشكل منطقي، مثل القرع الذي يتم ترتيبه حسب عدد محدود من الأحجام (صغير جدًا، صغير، متوسط، كبير، كبير جدًا، كبير جدًا جدًا).\n",
     "\n",
-    "![الانحدار متعدد الفئات مقابل الرتبي](../../../../../../translated_images/ar/multinomial-vs-ordinal.36701b4850e37d86.png)\n",
+    "![الانحدار متعدد الفئات مقابل الرتبي](../../../../../../translated_images/ar/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **المتغيرات لا يجب أن تكون مترابطة**\n",
     "\n",
diff --git a/translations/ar/2-Regression/README.md b/translations/ar/2-Regression/README.md
index 04fa8b8ed..cadd989b6 100644
--- a/translations/ar/2-Regression/README.md
+++ b/translations/ar/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في أمريكا الشمالية، غالبًا ما يتم نحت القرع إلى وجوه مخيفة للاحتفال بعيد الهالوين. دعونا نكتشف المزيد عن هذه الخضروات المثيرة!
 
-![jack-o-lanterns](../../../translated_images/ar/jack-o-lanterns.181c661a9212457d.jpg)
+![jack-o-lanterns](../../../translated_images/ar/jack-o-lanterns.181c661a9212457d.webp)
 > صورة بواسطة <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> على <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## ما ستتعلمه
diff --git a/translations/ar/3-Web-App/README.md b/translations/ar/3-Web-App/README.md
index ad836ee63..df57f57f5 100644
--- a/translations/ar/3-Web-App/README.md
+++ b/translations/ar/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في هذا القسم من المنهج، ستتعرف على موضوع عملي في التعلم الآلي: كيفية حفظ نموذج Scikit-learn كملف يمكن استخدامه لإجراء التنبؤات داخل تطبيق ويب. بمجرد حفظ النموذج، ستتعلم كيفية استخدامه في تطبيق ويب مبني باستخدام Flask. ستقوم أولاً بإنشاء نموذج باستخدام بعض البيانات المتعلقة بمشاهدات الأجسام الطائرة المجهولة (UFO)! بعد ذلك، ستبني تطبيق ويب يسمح لك بإدخال عدد من الثواني مع قيمة خط العرض وخط الطول للتنبؤ بالدولة التي أبلغت عن رؤية جسم طائر مجهول.
 
-![UFO Parking](../../../translated_images/ar/ufo.9e787f5161da9d4d.jpg)
+![UFO Parking](../../../translated_images/ar/ufo.9e787f5161da9d4d.webp)
 
 صورة بواسطة <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> على <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
diff --git a/translations/ar/4-Classification/README.md b/translations/ar/4-Classification/README.md
index 6fa4f17da..7c3472a7b 100644
--- a/translations/ar/4-Classification/README.md
+++ b/translations/ar/4-Classification/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في آسيا والهند، تتميز تقاليد الطعام بتنوعها الكبير ولذتها الفائقة! دعونا نلقي نظرة على بيانات حول المأكولات الإقليمية لفهم مكوناتها بشكل أفضل.
 
-![بائع طعام تايلاندي](../../../translated_images/ar/thai-food.c47a7a7f9f05c218.jpg)
+![بائع طعام تايلاندي](../../../translated_images/ar/thai-food.c47a7a7f9f05c218.webp)
 > الصورة بواسطة <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> على <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## ما الذي ستتعلمه
diff --git a/translations/ar/5-Clustering/README.md b/translations/ar/5-Clustering/README.md
index 5d5657ad1..143143b2a 100644
--- a/translations/ar/5-Clustering/README.md
+++ b/translations/ar/5-Clustering/README.md
@@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 الجمهور النيجيري المتنوع لديه أذواق موسيقية متنوعة. باستخدام البيانات المستخرجة من Spotify (مستوحاة من [هذه المقالة](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421))، دعونا نلقي نظرة على بعض الموسيقى الشعبية في نيجيريا. تتضمن هذه المجموعة من البيانات معلومات حول درجات "القابلية للرقص"، "الصوتية"، مستوى الصوت، "الكلامية"، الشعبية والطاقة للأغاني المختلفة. سيكون من المثير اكتشاف الأنماط في هذه البيانات!
 
-![جهاز تشغيل الأسطوانات](../../../translated_images/ar/turntable.f2b86b13c53302dc.jpg)
+![جهاز تشغيل الأسطوانات](../../../translated_images/ar/turntable.f2b86b13c53302dc.webp)
 
 > صورة بواسطة <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> على <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/ar/6-NLP/README.md b/translations/ar/6-NLP/README.md
index d595ae631..992ced73a 100644
--- a/translations/ar/6-NLP/README.md
+++ b/translations/ar/6-NLP/README.md
@@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في هذه الدروس، سنتعلم أساسيات معالجة اللغة الطبيعية من خلال بناء روبوتات محادثة صغيرة لفهم كيف يساعد التعلم الآلي في جعل هذه المحادثات أكثر "ذكاءً". ستعود بالزمن إلى الوراء، وتتحدث مع إليزابيث بينيت والسيد دارسي من رواية جين أوستن الكلاسيكية، **كبرياء وتحامل**، التي نُشرت عام 1813. بعد ذلك، ستُعمّق معرفتك من خلال تعلم تحليل المشاعر عبر مراجعات الفنادق في أوروبا.
 
-![كتاب كبرياء وتحامل وشاي](../../../translated_images/ar/p&p.279f1c49ecd88941.jpg)
+![كتاب كبرياء وتحامل وشاي](../../../translated_images/ar/p&p.279f1c49ecd88941.webp)
 > صورة بواسطة <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> على <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## الدروس
diff --git a/translations/ar/7-TimeSeries/README.md b/translations/ar/7-TimeSeries/README.md
index 034d92726..a8d68a688 100644
--- a/translations/ar/7-TimeSeries/README.md
+++ b/translations/ar/7-TimeSeries/README.md
@@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 تركيزنا الإقليمي هو استخدام الكهرباء عالميًا، وهو مجموعة بيانات مثيرة للاهتمام للتعلم عن كيفية التنبؤ باستخدام الطاقة في المستقبل بناءً على أنماط الأحمال السابقة. يمكنك أن ترى كيف يمكن أن يكون هذا النوع من التنبؤ مفيدًا للغاية في بيئة الأعمال.
 
-![شبكة الكهرباء](../../../translated_images/ar/electric-grid.0c21d5214db09ffa.jpg)
+![شبكة الكهرباء](../../../translated_images/ar/electric-grid.0c21d5214db09ffa.webp)
 
 صورة بواسطة [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) لأبراج كهرباء على طريق في راجستان على [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
 
diff --git a/translations/ar/8-Reinforcement/README.md b/translations/ar/8-Reinforcement/README.md
index 0061bf96c..351f6e0a1 100644
--- a/translations/ar/8-Reinforcement/README.md
+++ b/translations/ar/8-Reinforcement/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 تخيل أن لديك بيئة محاكاة مثل سوق الأسهم. ماذا يحدث إذا فرضت تنظيمًا معينًا؟ هل سيكون له تأثير إيجابي أم سلبي؟ إذا حدث شيء سلبي، عليك أن تأخذ هذا _التعزيز السلبي_، تتعلم منه، وتغير المسار. وإذا كان هناك نتيجة إيجابية، عليك أن تبني على هذا _التعزيز الإيجابي_.
 
-![بيتر والذئب](../../../translated_images/ar/peter.779730f9ba3a8a8d.png)
+![بيتر والذئب](../../../translated_images/ar/peter.779730f9ba3a8a8d.webp)
 
 > بيتر وأصدقاؤه بحاجة للهروب من الذئب الجائع! الصورة بواسطة [جين لوبر](https://twitter.com/jenlooper)
 
diff --git a/translations/ar/9-Real-World/README.md b/translations/ar/9-Real-World/README.md
index edf614048..1c3340ac2 100644
--- a/translations/ar/9-Real-World/README.md
+++ b/translations/ar/9-Real-World/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 في هذا القسم من المنهج الدراسي، ستتعرف على بعض تطبيقات العالم الحقيقي لتعلم الآلة الكلاسيكي. لقد قمنا بالبحث عبر الإنترنت للعثور على أوراق بحثية ومقالات حول تطبيقات استخدمت هذه الاستراتيجيات، مع تجنب الشبكات العصبية، التعلم العميق والذكاء الاصطناعي قدر الإمكان. تعرف على كيفية استخدام تعلم الآلة في أنظمة الأعمال، التطبيقات البيئية، التمويل، الفنون والثقافة، والمزيد.
 
-![chess](../../../translated_images/ar/chess.e704a268781bdad8.jpg)
+![chess](../../../translated_images/ar/chess.e704a268781bdad8.webp)
 
 > صورة بواسطة <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> على <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/ar/README.md b/translations/ar/README.md
index a58b0493b..30350f58b 100644
--- a/translations/ar/README.md
+++ b/translations/ar/README.md
@@ -1,169 +1,176 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T10:09:51+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T08:11:02+00:00",
   "source_file": "README.md",
   "language_code": "ar"
 }
 -->
-[![ترخيص GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
-[![مساهمو GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
-[![مشكلات GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
-[![طلبات سحب GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![مرحبًا بالـ PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![المتابعون على GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![Forks على GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![نجوم GitHub](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/)
 
 ### 🌐 دعم متعدد اللغات
 
-#### مدعوم عبر إجراء GitHub (مؤتمت ومحدّث دائمًا)
+#### مدعوم عبر إجراء GitHub (آلي ودائم التحديث)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[العربية](./README.md) | [البنغالية](../bn/README.md) | [البلغارية](../bg/README.md) | [البرمية (ميانمار)](../my/README.md) | [الصينية (مبسطة)](../zh/README.md) | [الصينية (تقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (تقليدية، ماكاو)](../mo/README.md) | [الصينية (تقليدية، تايوان)](../tw/README.md) | [الكرواتية](../hr/README.md) | [التشيكية](../cs/README.md) | [الدنماركية](../da/README.md) | [الهولندية](../nl/README.md) | [الإستونية](../et/README.md) | [الفنلندية](../fi/README.md) | [الفرنسية](../fr/README.md) | [الألمانية](../de/README.md) | [اليونانية](../el/README.md) | [العبرية](../he/README.md) | [الهندية](../hi/README.md) | [الهنغارية](../hu/README.md) | [الإندونيسية](../id/README.md) | [الإيطالية](../it/README.md) | [اليابانية](../ja/README.md) | [الكانادية](../kn/README.md) | [الكورية](../ko/README.md) | [اللتوانية](../lt/README.md) | [الماليزية](../ms/README.md) | [المالايالامية](../ml/README.md) | [الماراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [البيجن النيجيري](../pcm/README.md) | [النرويجية](../no/README.md) | [الفارسية (الفارسي)](../fa/README.md) | [البولندية](../pl/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البنجابية (Gurmukhi)](../pa/README.md) | [الرومانية](../ro/README.md) | [الروسية](../ru/README.md) | [الصربية (السيريلية)](../sr/README.md) | [السلوفاكية](../sk/README.md) | [السلوفينية](../sl/README.md) | [الإسبانية](../es/README.md) | [السواحيلية](../sw/README.md) | [السويدية](../sv/README.md) | [التاغالوغية (الفلبينية)](../tl/README.md) | [التاميلية](../ta/README.md) | [التيلوغو](../te/README.md) | [التايلاندية](../th/README.md) | [التركية](../tr/README.md) | [الأوكرانية](../uk/README.md) | [الأردية](../ur/README.md) | [الفيتنامية](../vi/README.md)
+[العربية](./README.md) | [البنغالية](../bn/README.md) | [البلغارية](../bg/README.md) | [البورمية (ميانمار)](../my/README.md) | [الصينية (المبسطة)](../zh/README.md) | [الصينية (التقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (التقليدية، ماكاو)](../mo/README.md) | [الصينية (التقليدية، تايوان)](../tw/README.md) | [الكرواتية](../hr/README.md) | [التشيكية](../cs/README.md) | [الدنماركية](../da/README.md) | [الهولندية](../nl/README.md) | [الإستونية](../et/README.md) | [الفنلندية](../fi/README.md) | [الفرنسية](../fr/README.md) | [الألمانية](../de/README.md) | [اليونانية](../el/README.md) | [العبرية](../he/README.md) | [الهندية](../hi/README.md) | [الهنغارية](../hu/README.md) | [الإندونيسية](../id/README.md) | [الإيطالية](../it/README.md) | [اليابانية](../ja/README.md) | [الكانادية](../kn/README.md) | [الكورية](../ko/README.md) | [الليتوانية](../lt/README.md) | [الماليزية](../ms/README.md) | [المالايالامية](../ml/README.md) | [المراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [اللهجة النيجرية](../pcm/README.md) | [النرويجية](../no/README.md) | [الفارسية (فارسي)](../fa/README.md) | [البولندية](../pl/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البنجابية (جرمخي)](../pa/README.md) | [الرومانية](../ro/README.md) | [الروسية](../ru/README.md) | [الصربية (السيريلية)](../sr/README.md) | [السلوفاكية](../sk/README.md) | [السلوفينية](../sl/README.md) | [الإسبانية](../es/README.md) | [السواحلية](../sw/README.md) | [السويدية](../sv/README.md) | [التاغالوغية (الفلبينية)](../tl/README.md) | [التاميلية](../ta/README.md) | [التيلجو](../te/README.md) | [التايلاندية](../th/README.md) | [التركية](../tr/README.md) | [الأوكرانية](../uk/README.md) | [الأردية](../ur/README.md) | [الفيتنامية](../vi/README.md)
+
+> **هل تفضل النسخ محليًا؟**
+
+> يتضمن هذا المستودع أكثر من 50 ترجمة للغات مختلفة مما يزيد كثيرًا من حجم التنزيل. للنسخ دون الترجمات، استخدم الفحص الجزئي:
+> ```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'
+> ```
+> يمنحك هذا كل ما تحتاجه لإكمال الدورة مع تنزيل أسرع بكثير.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### انضم إلى مجتمعنا
 
-[![خادم Microsoft Foundry في Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-نقوم بتشغيل سلسلة Learn with AI على Discord، تعرف على المزيد وانضم إلينا في [Learn with AI Series](https://aka.ms/learnwithai/discord) من 18 إلى 30 سبتمبر 2025. ستحصل على نصائح وحيل لاستخدام GitHub Copilot في علم البيانات.
+لدينا سلسلة تعلم عبر Discord مع الذكاء الاصطناعي مستمرة، تعرف أكثر وانضم إلينا في [سلسلة التعلم مع الذكاء الاصطناعي](https://aka.ms/learnwithai/discord) من 18 إلى 30 سبتمبر 2025. ستحصل على نصائح وتقنيات لاستخدام GitHub Copilot لعلوم البيانات.
 
-![سلسلة Learn with AI](../../translated_images/ar/3.9b58fd8d6c373c20.png)
+![سلسلة التعلم مع الذكاء الاصطناعي](../../../../translated_images/ar/3.9b58fd8d6c373c20.webp)
 
 # تعلم الآلة للمبتدئين - منهج دراسي
 
-> 🌍 سافر حول العالم بينما نستكشف تعلم الآلة من خلال ثقافات العالم 🌍
+> 🌍 سافر حول العالم أثناء استكشافنا لتعلم الآلة عبر ثقافات العالم 🌍
 
-يسعد فريق Cloud Advocates في Microsoft أن يقدم منهجًا دراسيًا مدته 12 أسبوعًا يتضمن 26 درسًا حول موضوع **تعلم الآلة**. في هذا المنهج، ستتعلم ما يُطلق عليه أحيانًا **تعلم الآلة الكلاسيكي**، باستخدام مكتبة Scikit-learn بشكل أساسي وتجنب التعلم العميق، الذي يتم تغطيته في [منهج "الذكاء الاصطناعي للمبتدئين"](https://aka.ms/ai4beginners). يمكنك إقران هذه الدروس مع [منهج "علم البيانات للمبتدئين"](https://aka.ms/ds4beginners) أيضًا!
+يسر فريق مروجي السحابة في مايكروسوفت أن يقدم منهجًا لمدة 12 أسبوعًا يتضمن 26 درسًا حول **تعلم الآلة**. في هذا المنهج، ستتعلم ما يسمى أحيانًا بـ **التعلم الآلي الكلاسيكي**، باستخدام مكتبة Scikit-learn بشكل أساسي وتجنب التعلم العميق، الذي يتم تغطيته في [منهج الذكاء الاصطناعي للمبتدئين](https://aka.ms/ai4beginners). يمكنك كذلك دمج هذه الدروس مع منهج ['علوم البيانات للمبتدئين'](https://aka.ms/ds4beginners)!
 
-سافر معنا حول العالم بينما نطبق هذه التقنيات الكلاسيكية على بيانات من مناطق متعددة في العالم. يتضمن كل درس اختبارات قبل وبعد الدرس، وإرشادات مكتوبة لإكمال الدرس، وحلًا، ومهمة، والمزيد. تتيح لنا منهجية التعلم القائمة على المشاريع أن تتعلم أثناء البناء، وهي طريقة مثبتة لجعل المهارات الجديدة "تثبت".
+سافر معنا حول العالم لتطبيق هذه التقنيات الكلاسيكية على بيانات من مناطق عديدة في العالم. تتضمن كل درس اختبارات قبليّة وبعديّة، تعليمات مكتوبة لإكمال الدرس، حلاً، واجبًا، وأكثر. تسمح منهجيتنا المعتمدة على المشاريع لك بالتعلم أثناء البناء، وهي طريقة مثبتة لترسيخ المهارات الجديدة.
 
-**✍️ شكر حار لمؤلفينا** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
+**✍️ شكر جزيل لكل من شارك في الكتابة:** جين لوبر، ستيفن هاول، فرانسيسكا لازيري، تومومي إيمورا، كاسي بريفيو، دميتري سوشنيكوف، كريس نورينغ، أنيربان موخرجي، أورنيلا ألتونيان، روث ياكوبو آمي بويد
 
-**🎨 شكر أيضًا لرسامينا** Tomomi Imura, Dasani Madipalli, and Jen Looper
+**🎨 شكر خاص أيضًا للرسامين:** تومومي إيمورا، داساني ماديبالي، وجين لوبر
 
-**🙏 شكر خاص 🙏 لمؤلفي ومراجعي ومساهمي المحتوى من سفراء طلاب Microsoft**، ولا سيما Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, و Snigdha Agarwal
+**🙏 شكر خاص 🙏 لسفراء الطلاب في مايكروسوفت من المؤلفين والمراجعين ومساهمي المحتوى، وبالأخص:** ريشيت داجلي، محمد سكيب خان إينان، روهان راج، ألكساندرو بيتريسكو، أبهيشيك جايسوال، نوبرين تاباسوم، إيوان سامويلا، وسنيغدا أغاروال
 
-**🤩 امتنان إضافي إلى سفراء طلاب Microsoft Eric Wanjau, Jasleen Sondhi, و Vidushi Gupta لدروس R الخاصة بنا!**
+**🤩 امتنان إضافي لسفراء الطلاب مايكروسوفت إريك وانجاو، جاسلين سوندهى، وفيدوشي جوبتا لدروس R الخاصة بنا!**
 
 # البدء
 
 اتبع هذه الخطوات:
-1. **استنسخ المستودع (Fork the Repository)**: Click on the "Fork" button at the top-right corner of this page.
-2. **استنساخ المستودع (Clone the Repository)**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+1. **استنسخ المستودع**: انقر على زر "Fork" في أعلى يمين هذه الصفحة.
+2. **انسخ المستودع**: `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [اعثر على جميع الموارد الإضافية لهذه الدورة في مجموعة Microsoft Learn الخاصة بنا](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [اعثر على جميع الموارد الإضافية لهذه الدورة في مجموعة مايكروسوفت ليرن](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **هل تحتاج مساعدة؟** اطلع على [دليل استكشاف الأخطاء وإصلاحها](TROUBLESHOOTING.md) للحصول على حلول للمشكلات الشائعة المتعلقة بالتثبيت والإعداد وتشغيل الدروس.
+> 🔧 **بحاجة للمساعدة؟** تفقد [دليل استكشاف المشكلات](TROUBLESHOOTING.md) لحلول المشاكل الشائعة في التثبيت، الإعداد، وتشغيل الدروس.
 
+**[الطلاب](https://aka.ms/student-page)**، لاستخدام هذا المنهج، استنسخ المستودع بالكامل إلى حساب GitHub الخاص بك وأكمل التمارين بنفسك أو ضمن مجموعة:
 
-**[الطلاب](https://aka.ms/student-page)**، لاستخدام هذا المنهج، انسخ المستودع بأكمله إلى حساب GitHub الخاص بك وأكمل التمارين بنفسك أو مع مجموعة:
+- ابدأ بالاختبار القبلي للمحاضرة.
+- اقرأ المحاضرة وأكمل الأنشطة، مع التوقف والتفكير عند كل اختبار معرفة.
+- حاول إنشاء المشاريع من خلال فهم الدروس بدلاً من تشغيل كود الحل؛ مع ذلك، يتوفر كود الحل في مجلد `/solution` في كل درس معتمد على المشروع.
+- خذ الاختبار البعدي للمحاضرة.
+- أتمم التحدي.
+- أتمم الواجب.
+- بعد إكمال مجموعة الدروس، زر [لوحة النقاش](https://github.com/microsoft/ML-For-Beginners/discussions) و"تعلم بصوت عالٍ" عبر ملء تقييم PAT المناسب. تقييم 'PAT' هو أداة تقييم تقدم تملأها لتطوير تعلمك. يمكنك أيضًا التفاعل مع تقييمات PAT الأخرى لنتعلم معًا.
 
-- ابدأ باختبار تمهيدي قبل المحاضرة.
-- اقرأ المحاضرة وأكمل الأنشطة، وتوقف وفكر عند كل نقطة تحقق معرفي.
-- حاول إنشاء المشاريع من خلال فهم الدروس بدلًا من تشغيل كود الحل مباشرة؛ ومع ذلك يتوفر هذا الكود في مجلدات `/solution` في كل درس موجه بالمشروع.
-- أكمل اختبار ما بعد المحاضرة.
-- أكمل التحدي.
-- أكمل الواجب.
-- بعد إكمال مجموعة دروس، قم بزيارة [لوحة النقاش](https://github.com/microsoft/ML-For-Beginners/discussions) و"تعلّم بصوت عالٍ" بملء نموذج التقييم PAT المناسب. الـ 'PAT' هي أداة تقييم التقدم وهي مقياس تملأه لتعزيز تعلمك. يمكنك أيضًا التفاعل مع PATs الأخرى حتى نتعلم سويًا.
+> للدراسة المتقدمة، ننصح بمتابعة هذه [وحدات ومسارات التعلم على Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
 
-> لمزيد من الدراسة، نوصي بمتابعة هذه الوحدات ومسارات التعلم في [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott).
-
-**المعلمون**, لقد قمنا [بتضمين بعض الاقتراحات](for-teachers.md) حول كيفية استخدام هذا المنهج.
+**المعلمون**، لقد أدرجنا بعض الاقتراحات في [ملف للاستخدام التعليمي](for-teachers.md) حول كيفية استخدام هذا المنهج.
 
 ---
 
-## شروحات الفيديو
+## جولات فيديو توضيحية
 
-بعض الدروس متاحة كفيديو قصير. يمكنك العثور على كل هذه الفيديوهات مضمّنة داخل الدروس، أو على [قائمة تشغيل ML للمبتدئين على قناة Microsoft Developer على YouTube](https://aka.ms/ml-beginners-videos) بالنقر على الصورة أدناه.
+بعض الدروس متوفرة على شكل فيديوهات قصيرة. يمكنك العثور على جميع هذه الفيديوهات ضمن الدروس، أو على [قائمة تشغيل ML for Beginners على قناة مايكروسوفت ديفلوبر على يوتيوب](https://aka.ms/ml-beginners-videos) بالنقر على الصورة أدناه.
 
-[![لافتة ML للمبتدئين](../../translated_images/ar/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![بانر تعلم الآلة للمبتدئين](../../../../translated_images/ar/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## تعرّف على الفريق
+## فريق العمل
 
 [![فيديو ترويجي](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
-**GIF بواسطة** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
+**GIF بواسطة** [موهيت جيسال](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 انقر على الصورة أعلاه لمشاهدة فيديو عن المشروع والأشخاص الذين أنشأوه!
+> 🎥 اضغط على الصورة أعلاه لمشاهدة فيديو عن المشروع والأشخاص الذين أنشأوه!
 
 ---
 
-## المنهجية التربوية
+## المنهجية التعليمية
 
-اخترنا مبدأين بيداغوجيين أثناء بناء هذا المنهج: التأكد من أنه عملي **قائم على المشاريع** وأنه يتضمن **اختبارات متكررة**. بالإضافة إلى ذلك، يحتوي هذا المنهج على **موضوع** مشترك لمنحه تماسكًا.
+اخترنا مبدأين تربويين أثناء بناء هذا المنهج: ضمان كونه **مبنيًا على المشاريع عمليًا**، وأن يتضمن **اختبارات متكررة**. بالإضافة إلى ذلك، يحتوي هذا المنهج على **موضوع مشترك** ليمنحه التماسك.
 
-من خلال التأكد من توافق المحتوى مع المشاريع، يصبح المسار أكثر جاذبية للطلاب ويزداد احتفاظهم بالمفاهيم. بالإضافة إلى ذلك، يضع اختبار منخفض المخاطر قبل الحصة نية الطالب نحو تعلم موضوع ما، في حين يضمن اختبار ثانٍ بعد الحصة مزيدًا من الاحتفاظ. صُمم هذا المنهج ليكون مرنًا وممتعًا ويمكن اتباعه ككل أو جزئيًا. تبدأ المشاريع صغيرة وتصبح أكثر تعقيدًا تدريجيًا بنهاية دورة الـ 12 أسبوعًا. كما يتضمن هذا المنهج خاتمة حول تطبيقات تعلم الآلة في العالم الحقيقي، والتي يمكن استخدامها كدرجة إضافية أو كقاعدة للنقاش.
+بضمان توافق المحتوى مع المشاريع، تصبح العملية أكثر إثارة للطلاب ويزداد تثبيت المفاهيم. بالإضافة إلى ذلك، يحدد اختبار منخفض المخاطر قبل الدرس نية الطالب نحو تعلم موضوع ما، بينما يضمن اختبار آخر بعد الدرس تثبيتًا أكبر. صُمم هذا المنهج ليكون مرنًا وممتعًا ويمكن أخذه ككل أو جزئيًا. تبدأ المشاريع صغيرة وتزداد تعقيدًا بنهاية دورة 12 أسبوعًا. كما يحتوي المنهج على خاتمة حول تطبيقات تعلم الآلة في العالم الحقيقي، يمكن استخدامها كنشاط إضافي أو كأساس للنقاش.
 
-> اطلع على [مدونة قواعد السلوك](CODE_OF_CONDUCT.md)، و[إرشادات المساهمة](CONTRIBUTING.md)، و[الترجمة](TRANSLATIONS.md)، و[دليل استكشاف الأخطاء وإصلاحها](TROUBLESHOOTING.md). نرحب بتغذيتكم الراجعة البنّاءة!
+> تجدون إرشاداتنا الخاصة ب[مدونة السلوك](CODE_OF_CONDUCT.md)، و[المساهمة](CONTRIBUTING.md)، و[الترجمة](TRANSLATIONS.md)، و[استكشاف المشكلات](TROUBLESHOOTING.md). نحن نرحب بملاحظاتكم البناءة!
 
 ## كل درس يتضمن
 
-- مذكرة رسومية اختيارية
-- فيديو تكميلي اختياري
-- عرض فيديو توضيحي (بعض الدروس فقط)
-- [اختبار إحماء قبل المحاضرة](https://ff-quizzes.netlify.app/en/ml/)
-- درس مكتوب
-- للدروس القائمة على المشاريع، أدلة خطوة بخطوة حول كيفية بناء المشروع
-- اختبارات للتحقق من الفهم
-- تحدٍ
-- قراءة تكملية
-- واجب
-- [اختبار ما بعد المحاضرة](https://ff-quizzes.netlify.app/en/ml/)
-
-> **ملاحظة حول اللغات**: تم كتابة هذه الدروس في المقام الأول بلغة Python، لكن العديد منها متاح أيضًا بلغة R. لإكمال درس R، انتقل إلى مجلد `/solution` وابحث عن دروس R. تتضمن امتداد .rmd الذي يمثل ملف **R Markdown** والذي يمكن تعريفه ببساطة على أنه تضمين لـ `code chunks` (بلغة R أو لغات أخرى) و `YAML header` (الذي يوجّه كيفية تنسيق المخرجات مثل PDF) في `Markdown document`. لذلك، يعمل كإطار تأليف نموذجي لعلم البيانات لأنه يسمح لك بدمج كودك ومخرجاته وأفكارك عن طريق كتابتها في Markdown. علاوة على ذلك، يمكن عرض مستندات R Markdown إلى صيغ إخراج مثل PDF أو HTML أو Word.
-
-> **ملاحظة حول الاختبارات**: جميع الاختبارات موجودة في [مجلد تطبيق الاختبار](../../quiz-app)، لعدد إجمالي 52 اختبارًا، كل منها يحتوي على ثلاثة أسئلة. يتم ربطها من داخل الدروس لكن يمكن تشغيل تطبيق الاختبار محليًا؛ اتبع التعليمات في مجلد `quiz-app` لاستضافته محليًا أو نشره على Azure.
-
-| رقم الدرس |                             الموضوع                              |                   مجموعة الدروس                   | أهداف التعلم                                                                                                             |                                                              الدرس المرتبط                                                               |                        المؤلف                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                مقدمة في تعلم الآلة                |      [مقدمة](1-Introduction/README.md)       | تعلّم المفاهيم الأساسية وراء تعلم الآلة                                                                                |                                             [الدرس](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                تاريخ تعلم الآلة                 |      [مقدمة](1-Introduction/README.md)       | تعرّف على التاريخ الكامن وراء هذا المجال                                                                                         |                                            [الدرس](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen و Amy                      |
-|      03       |                 العدالة وتعلم الآلة                  |      [مقدمة](1-Introduction/README.md)       | ما هي القضايا الفلسفية الهامة حول العدالة التي يجب أن يأخذها الطلاب بعين الاعتبار عند بناء وتطبيق نماذج تعلم الآلة؟ |                                              [الدرس](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                تقنيات تعلم الآلة                 |      [مقدمة](1-Introduction/README.md)       | ما هي التقنيات التي يستخدمها باحثو تعلم الآلة لبناء النماذج؟                                                                       |                                          [الدرس](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris و Jen                     |
-|      05       |                   مقدمة في الانحدار                   |        [الانحدار](2-Regression/README.md)         | ابدأ مع Python و Scikit-learn لنماذج الانحدار                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                أسعار اليقطين في أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | تصور ونظّف البيانات كتحضير لتعلم الآلة                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                أسعار اليقطين في أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | بناء نماذج الانحدار الخطي والمتعدد الحدود                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen و Dmitry • Eric Wanjau       |
-|      08       |                أسعار اليقطين في أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | بناء نموذج الانحدار اللوجستي                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          تطبيق ويب 🔌                          |           [تطبيق ويب](3-Web-App/README.md)            | بناء تطبيق ويب لاستخدام نموذجك المدرب                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 مقدمة في التصنيف                 |    [التصنيف](4-Classification/README.md)     | نظّف، حضّر، وصوّر بياناتك؛ مقدمة في التصنيف                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen و Cassie • Eric Wanjau |
-|      11       |             مأكولات آسيوية وهندية لذيذة 🍜             |    [التصنيف](4-Classification/README.md)     | مقدمة للمصنّفات                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen و Cassie • Eric Wanjau |
-|      12       |             مأكولات آسيوية وهندية لذيذة 🍜             |    [التصنيف](4-Classification/README.md)     | المزيد من المصنّفات                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen و Cassie • Eric Wanjau |
-|      13       |             مأكولات آسيوية وهندية لذيذة 🍜             |    [التصنيف](4-Classification/README.md)     | بناء تطبيق ويب موصيّ باستخدام نموذجك                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   مقدمة في التجميع                   |        [التجميع](5-Clustering/README.md)         | نظّف، حضّر، وصوّر بياناتك؛ مقدمة في التجميع                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              استكشاف الأذواق الموسيقية النيجيرية 🎧              |        [التجميع](5-Clustering/README.md)         | استكشاف طريقة التجميع K-Means                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        مقدمة في معالجة اللغة الطبيعية ☕️         |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تعلّم أساسيات معالجة اللغة الطبيعية ببناء روبوت بسيط                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      مهام معالجة اللغة الشائعة ☕️                      |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تعمّق في معرفتك بمعالجة اللغة من خلال فهم المهام الشائعة المطلوبة عند التعامل مع هياكل اللغة                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             الترجمة وتحليل المشاعر ♥️              |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | الترجمة وتحليل المشاعر مع Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  فنادق رومانسية في أوروبا ♥️                  |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تحليل المشاعر مع مراجعات فنادق 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  فنادق رومانسية في أوروبا ♥️                  |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تحليل المشاعر مع مراجعات فنادق 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            مقدمة في التنبؤ بالسلاسل الزمنية             |        [السلاسل الزمنية](7-TimeSeries/README.md)        | مقدمة في التنبؤ بالسلاسل الزمنية                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ استهلاك الطاقة العالمي ⚡️ - التنبؤ بالسلاسل الزمنية باستخدام ARIMA |        [السلاسل الزمنية](7-TimeSeries/README.md)        | التنبؤ بالسلاسل الزمنية باستخدام ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ استهلاك الطاقة العالمي ⚡️ - التنبؤ بالسلاسل الزمنية باستخدام SVR  |        [السلاسل الزمنية](7-TimeSeries/README.md)        | التنبؤ بالسلاسل الزمنية باستخدام Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             مقدمة في التعلم التعزيزي             | [التعلم التعزيزي](8-Reinforcement/README.md) | مقدمة في التعلم التعزيزي باستخدام Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 ساعد بيتر على تجنب الذئب! 🐺                  | [التعلم التعزيزي](8-Reinforcement/README.md) | بيئة Gym للتعلم التعزيزي                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  ملحق   |            سيناريوهات وتطبيقات تعلم الآلة في العالم الحقيقي            |      [ML in the Wild](9-Real-World/README.md)       | تطبيقات مثيرة وكاشفة في العالم الحقيقي لتعلم الآلة الكلاسيكي                                                               |                                             [الدرس](9-Real-World/1-Applications/README.md)                                              |                         الفريق                         |
-|  ملحق   |            تصحيح النماذج في تعلم الآلة باستخدام لوحة RAI          |      [ML in the Wild](9-Real-World/README.md)       | تصحيح نماذج تعلم الآلة باستخدام مكونات لوحة معلومات Responsible AI                                                              |                                             [الدرس](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [اعثر على جميع الموارد الإضافية لهذه الدورة في مجموعة Microsoft Learn الخاصة بنا](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- رسم تخطيطي اختياري  
+- فيديو تكميلي اختياري  
+- جولة فيديو توضيحية (بعض الدروس فقط)  
+- [اختبار تسخين قبلي للمحاضرة](https://ff-quizzes.netlify.app/en/ml/)  
+- درس مكتوب  
+- للدروس المبنية على المشاريع، دلائل خطوة بخطوة لبناء المشروع  
+- اختبارات معرفة  
+- تحدي  
+- قراءة إضافية  
+- واجب  
+- [اختبار بعد المحاضرة](https://ff-quizzes.netlify.app/en/ml/)
+
+> **ملاحظة حول اللغات**: هذه الدروس مكتوبة أساسًا بلغة Python، لكن كثيرًا منها متاح أيضًا بلغة R. لإكمال درس R، اذهب إلى مجلد `/solution` وابحث عن دروس R. تحتوي على امتداد .rmd الذي يمثل ملف **R Markdown**، يمكن تعريفه ببساطة على أنه تضمين لـ`كتل كود` (بـ R أو لغات أخرى) ورأس `YAML` (يوجه كيفية تنسيق المخرجات مثل PDF) في مستند `Markdown`. وبالتالي، فهو إطار تأليف نموذجي لعلوم البيانات لأنه يسمح لك بدمج كودك، مخرجاته، وأفكارك عبر تدوينها في Markdown. علاوة على ذلك، يمكن تحويل مستندات R Markdown إلى صيغ مخرجات مثل PDF، HTML، أو Word.
+> **ملاحظة حول الاختبارات**: جميع الاختبارات موجودة في [مجلد تطبيق الاختبار](../../quiz-app)، حيث يوجد 52 اختبارًا مكونًا من ثلاثة أسئلة لكل منها. يتم ربطها من داخل الدروس ولكن يمكن تشغيل تطبيق الاختبار محليًا؛ اتبع التعليمات في مجلد `quiz-app` لاستضافة التطبيق محليًا أو نشره على Azure.
+
+| رقم الدرس |                             الموضوع                              |                   تجميع الدرس                   | الأهداف التعليمية                                                                                                             |                                                              الدرس المرتبط                                                               |                        المؤلف                        |
+| :-------: | :------------------------------------------------------------: | :---------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
+|    01     |                مقدمة في تعلم الآلة                |      [مقدمة](1-Introduction/README.md)       | تعلم المفاهيم الأساسية وراء تعلم الآلة                                                                                |                                             [درس](1-Introduction/1-intro-to-ML/README.md)                                             |                       محمد                       |
+|    02     |                تاريخ تعلم الآلة                 |      [مقدمة](1-Introduction/README.md)       | تعلم التاريخ الكامن وراء هذا المجال                                                                                         |                                            [درس](1-Introduction/2-history-of-ML/README.md)                                            |                     جين وآمي                      |
+|    03     |                 العدالة وتعلم الآلة                  |      [مقدمة](1-Introduction/README.md)       | ما هي القضايا الفلسفية المهمة المتعلقة بالعدالة التي يجب على الطلاب مراعاتها عند بناء وتطبيق نماذج تعلم الآلة؟ |                                              [درس](1-Introduction/3-fairness/README.md)                                               |                        تومومي                        |
+|    04     |                تقنيات تعلم الآلة                 |      [مقدمة](1-Introduction/README.md)       | ما هي التقنيات التي يستخدمها الباحثون في تعلم الآلة لبناء النماذج؟                                                                       |                                          [درس](1-Introduction/4-techniques-of-ML/README.md)                                           |                    كريس وجين                     |
+|    05     |                   مقدمة في الانحدار                   |        [الانحدار](2-Regression/README.md)         | ابدأ مع Python و Scikit-learn لنماذج الانحدار                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      جين • إريك وانجاو       |
+|    06     |                أسعار قرع أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | تصور وتنظيف البيانات للتحضير لتعلم الآلة                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      جين • إريك وانجاو       |
+|    07     |                أسعار قرع أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | بناء نماذج انحدار خطي ومتعدد الحدود                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      جين ودميتري • إريك وانجاو       |
+|    08     |                أسعار قرع أمريكا الشمالية 🎃                |        [الانحدار](2-Regression/README.md)         | بناء نموذج انحدار لوجستي                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      جين • إريك وانجاو       |
+|    09     |                          تطبيق ويب 🔌                          |           [تطبيق ويب](3-Web-App/README.md)            | بناء تطبيق ويب لاستخدام نموذجك المدرب                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         جين                          |
+|    10     |                 مقدمة في التصنيف                 |    [تصنيف](4-Classification/README.md)     | تنظيف، تحضير، وتصور بياناتك؛ مقدمة في التصنيف                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | جين وكاسي • إريك وانجاو |
+|    11     |             المأكولات الآسيوية والهندية اللذيذة 🍜             |    [تصنيف](4-Classification/README.md)     | مقدمة في المصنفات                                                                                                     | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | جين وكاسي • إريك وانجاو |
+|    12     |             المأكولات الآسيوية والهندية اللذيذة 🍜             |    [تصنيف](4-Classification/README.md)     | المزيد من المصنفات                                                                                                                | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | جين وكاسي • إريك وانجاو |
+|    13     |             المأكولات الآسيوية والهندية اللذيذة 🍜             |    [تصنيف](4-Classification/README.md)     | بناء تطبيق ويب للتوصية باستخدام نموذجك                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         جين                          |
+|    14     |                   مقدمة في التجميع                   |        [تجميع](5-Clustering/README.md)         | تنظيف، تحضير، وتصور بياناتك؛ مقدمة في التجميع                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      جين • إريك وانجاو       |
+|    15     |              استكشاف الأذواق الموسيقية النيجيرية 🎧              |        [تجميع](5-Clustering/README.md)         | استكشاف طريقة K-Means للتجميع                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      جين • إريك وانجاو       |
+|    16     |        مقدمة في معالجة اللغة الطبيعية ☕️         |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تعلّم الأساسيات عن NLP من خلال بناء بوت بسيط                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       ستيفن                        |
+|    17     |                      المهام الشائعة في NLP ☕️                      |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تعميق معرفتك في NLP من خلال فهم المهام الشائعة المطلوبة عند التعامل مع هياكل اللغة                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       ستيفن                        |
+|    18     |             الترجمة وتحليل المشاعر ♥️              |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | الترجمة وتحليل المشاعر مع جين أوستن                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       ستيفن                        |
+|    19     |                  الفنادق الرومانسية في أوروبا ♥️                  |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تحليل المشاعر مع مراجعات الفنادق 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       ستيفن                        |
+|    20     |                  الفنادق الرومانسية في أوروبا ♥️                  |   [معالجة اللغة الطبيعية](6-NLP/README.md)    | تحليل المشاعر مع مراجعات الفنادق 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       ستيفن                        |
+|    21     |            مقدمة في التنبؤ بالسلاسل الزمنية             |        [السلاسل الزمنية](7-TimeSeries/README.md)        | مقدمة في التنبؤ بالسلاسل الزمنية                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      فرانسيسكا                       |
+|    22     | ⚡️ استخدام الطاقة العالمي ⚡️ - التنبؤ بالسلاسل الزمنية مع ARIMA |        [السلاسل الزمنية](7-TimeSeries/README.md)        | التنبؤ بالسلاسل الزمنية مع ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      فرانسيسكا                       |
+|    23     |  ⚡️ استخدام الطاقة العالمي ⚡️ - التنبؤ بالسلاسل الزمنية مع SVR  |        [السلاسل الزمنية](7-TimeSeries/README.md)        | التنبؤ بالسلاسل الزمنية باستخدام مؤشر الدعم الانحداري                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       أنيربان                        |
+|    24     |             مقدمة في التعلم المعزز             | [التعلم المعزز](8-Reinforcement/README.md) | مقدمة في التعلم المعزز باستخدام Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        دمیتری                        |
+|    25     |                 ساعد بيتر على تجنب الذئب! 🐺                  | [التعلم المعزز](8-Reinforcement/README.md) | التعلم المعزز باستخدام Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        دمیتری                        |
+|  خاتمة   |            سيناريوهات وتطبيقات تعلم الآلة في العالم الحقيقي            |      [تعلم الآلة في العالم الحقيقي](9-Real-World/README.md)       | تطبيقات حقيقية مثيرة للكشف من تعلم الآلة الكلاسيكي                                                               |                                             [درس](9-Real-World/1-Applications/README.md)                                              |                         الفريق                         |
+|  خاتمة   |            تصحيح نموذج التعلم الآلي باستخدام لوحة تحكم RAI          |      [تعلم الآلة في العالم الحقيقي](9-Real-World/README.md)       | تصحيح نموذج تعلم الآلة باستخدام مكونات لوحة تحكم الذكاء المسؤول                                                              |                                             [درس](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         راث ياكوبو                       |
+
+> [ابحث عن جميع الموارد الإضافية لهذا المساق في مجموعة Microsoft Learn الخاصة بنا](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## الوصول دون اتصال
 
-يمكنك تشغيل هذه الوثائق دون اتصال باستخدام [Docsify](https://docsify.js.org/#/). قم بفورك لهذا المستودع، [قم بتثبيت Docsify](https://docsify.js.org/#/quickstart) على جهازك المحلي، ثم في المجلد الجذر لهذا المستودع اكتب `docsify serve`. سيتم تقديم الموقع على المنفذ 3000 على مضيفك المحلي: `localhost:3000`.
+يمكنك تشغيل هذه الوثائق دون اتصال باستخدام [Docsify](https://docsify.js.org/#/). قم بعمل نسخة من هذا المستودع، [ثبت Docsify](https://docsify.js.org/#/quickstart) على جهازك المحلي، ثم في المجلد الجذر لهذا المستودع، اكتب `docsify serve`. سيتم تقديم الموقع على المنفذ 3000 على جهازك المحلي: `localhost:3000`.
 
 ## ملفات PDF
 
-اعثر على ملف PDF للمنهج مع روابط [هنا](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
-
+اعثر على ملف PDF للمناهج الدراسية مع الروابط [هنا](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
-## 🎒 دورات أخرى 
+## 🎒 دورات أخرى
 
-فريقنا يقدم دورات أخرى! اطلع على:
+ينتج فريقنا دورات أخرى! تحقق من:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -172,24 +179,24 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
 
-### Azure / Edge / MCP / Agents
+### Azure / Edge / MCP / الوكلاء
 [![AZD للمبتدئين](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 للمبتدئين](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 للمبتدئين](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)
-[![وكلاء AI للمبتدئين](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)
+[![وكلاء الذكاء الاصطناعي للمبتدئين](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)
 
 ---
  
-### سلسلة الذكاء التوليدي
-[![الذكاء التوليدي للمبتدئين](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)
-[![الذكاء التوليدي (.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)
-[![الذكاء التوليدي (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)
-[![الذكاء التوليدي (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)
+### سلسلة الذكاء الاصطناعي التوليدي
+[![الذكاء الاصطناعي التوليدي للمبتدئين](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)
+[![الذكاء الاصطناعي التوليدي (.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)
+[![الذكاء الاصطناعي التوليدي (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)
+[![الذكاء الاصطناعي التوليدي (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)
 
 ---
  
 ### التعلم الأساسي
-[![تعلم الآلة للمبتدئين](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)
+[![التعلم الآلي للمبتدئين](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)
 [![علوم البيانات للمبتدئين](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)
 [![الذكاء الاصطناعي للمبتدئين](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)
 [![الأمن السيبراني للمبتدئين](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)
@@ -199,25 +206,25 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
  
-### سلسلة Copilot
-[![Copilot للبرمجة المشتركة بالذكاء الاصطناعي](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![Copilot لـ C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![مغامرة Copilot](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)
+### سلسلة المساعد
+[![مساعد البرمجة الزوجية بالذكاء الاصطناعي](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)
+[![المساعد لـ C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![مغامرة المساعد](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 -->
 
 ## الحصول على المساعدة
 
-إذا علقت أو كانت لديك أي أسئلة حول بناء تطبيقات الذكاء الاصطناعي، انضم إلى المتعلمين الآخرين والمطورين ذوي الخبرة في مناقشات MCP. إنه مجتمع داعم حيث تُرحب بالأسئلة وتُشارك المعرفة بحرية.
+إذا توقفت أو كانت لديك أي أسئلة حول بناء تطبيقات الذكاء الاصطناعي. انضم إلى المتعلمين الآخرين والمطورين ذوي الخبرة في مناقشات حول MCP. إنها مجتمع داعم حيث تُرحب بالأسئلة ويُشارك المعرفة بحرية.
 
-[![خادم Microsoft Foundry على Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-إذا كان لديك ملاحظات حول المنتج أو أخطاء أثناء البناء فقم بزيارة:
+إذا كان لديك ملاحظات على المنتج أو إذا ظهرت أخطاء أثناء البناء، قم بزيارة:
 
 [![منتدى مطوري Microsoft Foundry](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 -->
-إخلاء المسؤولية:
-تمت ترجمة هذه الوثيقة باستخدام خدمة الترجمة الآلية [Co-op Translator] (https://github.com/Azure/co-op-translator). بينما نسعى إلى الدقة، يُرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار الوثيقة الأصلية بلغتها الأصلية المصدر الموثوق به. للمعلومات الحرجة، يوصى بالاستعانة بترجمة بشرية مهنية. نحن غير مسؤولين عن أي سوء فهم أو تفسير ينشأ عن استخدام هذه الترجمة.
+**تنبيه**:
+تمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بالرغم من سعينا لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي والمعتمد. للمعلومات الهامة، يُنصح بالترجمة البشرية المهنية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/bg/README.md b/translations/bg/README.md
index 223113c47..655117b07 100644
--- a/translations/bg/README.md
+++ b/translations/bg/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Имаме поредица в Discord „Научи с ИИ“, която е в ход — научете повече и се присъединете към нас на [Серия „Научи с ИИ“](https://aka.ms/learnwithai/discord) от 18 до 30 септември 2025 г. Ще получите съвети и трикове за използване на GitHub Copilot за наука за данните.
 
-![Серия „Научи с ИИ“](../../translated_images/bg/3.9b58fd8d6c373c20.png)
+![Серия „Научи с ИИ“](../../translated_images/bg/3.9b58fd8d6c373c20.webp)
 
 # Машинно обучение за начинаещи - Учебна програма
 
@@ -80,7 +80,7 @@ Cloud Advocates в Microsoft с удоволствие предлагат 12-с
 
 Някои от уроците са налични като кратки видео форми. Можете да ги намерите в самите уроци или в [плейлиста ML for Beginners в канала Microsoft Developer в YouTube](https://aka.ms/ml-beginners-videos), като кликнете върху изображението по-долу.
 
-[![Банер 'ML for beginners'](../../translated_images/bg/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![Банер 'ML for beginners'](../../translated_images/bg/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/bn/1-Introduction/README.md b/translations/bn/1-Introduction/README.md
index 23bf33735..a02010b7f 100644
--- a/translations/bn/1-Introduction/README.md
+++ b/translations/bn/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 এই পাঠক্রমের এই অংশে, আপনি মেশিন লার্নিং ক্ষেত্রের মূল ধারণাগুলি, এটি কী, এর ইতিহাস এবং গবেষকরা এটি নিয়ে কাজ করার জন্য যে কৌশলগুলি ব্যবহার করেন তা সম্পর্কে জানতে পারবেন। চলুন একসাথে এই নতুন মেশিন লার্নিং-এর জগৎটি অন্বেষণ করি!
 
-![globe](../../../translated_images/bn/globe.59f26379ceb40428.jpg)
+![globe](../../../translated_images/bn/globe.59f26379ceb40428.webp)
 > ছবি <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">বিল অক্সফোর্ড</a> এর তোলা <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">আনস্প্ল্যাশ</a>-এ
   
 ### পাঠসমূহ
diff --git a/translations/bn/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/bn/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index 77bfc1fed..8ce4b651e 100644
--- a/translations/bn/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/bn/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -48,7 +48,7 @@
     "   width=\"630\"/>\n",
     "   <figcaption>চিত্রকর্ম: @allison_horst</figcaption>\n",
     "\n",
-    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/encouRage.e75d5fe0367fb913.jpg)<br>চিত্রকর্ম: @allison_horst-->\n"
+    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/encouRage.e75d5fe0367fb913.webp)<br>চিত্রকর্ম: @allison_horst-->\n"
    ],
    "metadata": {
     "id": "LWNNzfqd6feZ"
diff --git a/translations/bn/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/bn/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index fa239ef05..cce330c6b 100644
--- a/translations/bn/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/bn/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -50,7 +50,7 @@
     "   <figcaption>চিত্রকর্ম: @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/unruly_data.0eedc7ced92d2d91.jpg)<br>চিত্রকর্ম: \\@allison_horst-->\n"
+    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/unruly_data.0eedc7ced92d2d91.webp)<br>চিত্রকর্ম: \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "Pg5aexcOPqAZ"
@@ -231,7 +231,7 @@
     "   <figcaption>চিত্রকর্ম: @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/dplyr_wrangling.f5f99c64fd4580f1.png)<br/>চিত্রকর্ম: \\@allison_horst-->\n"
+    "<!--![চিত্রকর্ম: \\@allison_horst](../../../../../../translated_images/bn/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>চিত্রকর্ম: \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "o4jLY5-VZO2C"
@@ -534,7 +534,7 @@
     "   <figcaption>ইনফোগ্রাফিক: দাসানি মাদিপল্লি</figcaption>\n",
     "\n",
     "\n",
-    "<!--![ইনফোগ্রাফিক: দাসানি মাদিপল্লি](../../../../../../translated_images/bn/data-visualization.54e56dded7c1a804.png){width=\"600\"}-->\n",
+    "<!--![ইনফোগ্রাফিক: দাসানি মাদিপল্লি](../../../../../../translated_images/bn/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "একটি *জ্ঞানী* কথা আছে যা এভাবে বলা হয়:\n",
     "\n",
diff --git a/translations/bn/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/bn/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 30f7af75b..c0c7eef99 100644
--- a/translations/bn/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/bn/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -804,7 +804,7 @@
     "   <figcaption>ইনফোগ্রাফিক: দাসানি মাদিপল্লি</figcaption>\n",
     "\n",
     "\n",
-    "<!--![ইনফোগ্রাফিক: দাসানি মাদিপল্লি](../../../../../../translated_images/bn/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![ইনফোগ্রাফিক: দাসানি মাদিপল্লি](../../../../../../translated_images/bn/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "HOCqJXLTwtWI"
diff --git a/translations/bn/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/bn/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index 876d47117..307048bdd 100644
--- a/translations/bn/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/bn/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## লজিস্টিক রিগ্রেশন মডেল তৈরি করুন - পাঠ ৪\n",
     "\n",
-    "![লজিস্টিক বনাম লিনিয়ার রিগ্রেশন ইনফোগ্রাফিক](../../../../../../translated_images/bn/linear-vs-logistic.ba180bf95e7ee667.png)\n",
+    "![লজিস্টিক বনাম লিনিয়ার রিগ্রেশন ইনফোগ্রাফিক](../../../../../../translated_images/bn/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[পাঠের পূর্ববর্তী কুইজ](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "লজিস্টিক রিগ্রেশন লিনিয়ার রিগ্রেশনের মতো একই বৈশিষ্ট্য প্রদান করে না। প্রথমটি একটি `বাইনারি বিভাগ` (\"কমলা বা কমলা নয়\") সম্পর্কে পূর্বাভাস দেয়, যেখানে দ্বিতীয়টি `ধারাবাহিক মান` পূর্বাভাস দিতে সক্ষম, যেমন কুমড়ার উৎপত্তি এবং ফসল তোলার সময় দেওয়া হলে, *এর দাম কতটা বাড়বে*।\n",
     "\n",
-    "![দাসানি মাদিপালির ইনফোগ্রাফিক](../../../../../../translated_images/bn/pumpkin-classifier.562771f104ad5436.png)\n",
+    "![দাসানি মাদিপালির ইনফোগ্রাফিক](../../../../../../translated_images/bn/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### অন্যান্য শ্রেণীবিভাগ\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **অর্ডিনাল**, যেখানে ক্রমানুসারে বিভাগ থাকে, যা আমাদের ফলাফলগুলোকে যৌক্তিকভাবে সাজাতে সাহায্য করে, যেমন আমাদের কুমড়াগুলো যা একটি নির্দিষ্ট সংখ্যক আকারে সাজানো থাকে (মিনি, ছোট, মাঝারি, বড়, এক্সএল, ডাবল এক্সএল)।\n",
     "\n",
-    "![মাল্টিনোমিয়াল বনাম অর্ডিনাল রিগ্রেশন](../../../../../../translated_images/bn/multinomial-vs-ordinal.36701b4850e37d86.png)\n",
+    "![মাল্টিনোমিয়াল বনাম অর্ডিনাল রিগ্রেশন](../../../../../../translated_images/bn/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **ভেরিয়েবলগুলোর মধ্যে সম্পর্ক থাকা জরুরি নয়**\n",
     "\n",
diff --git a/translations/bn/2-Regression/README.md b/translations/bn/2-Regression/README.md
index d8d581efe..aa46b299f 100644
--- a/translations/bn/2-Regression/README.md
+++ b/translations/bn/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 উত্তর আমেরিকায়, হ্যালোউইনের জন্য কুমড়া প্রায়ই ভয়ঙ্কর মুখে খোদাই করা হয়। চলুন এই চমৎকার সবজিগুলো সম্পর্কে আরও জানি!
 
-![জ্যাক-ও-ল্যান্টার্নস](../../../translated_images/bn/jack-o-lanterns.181c661a9212457d.jpg)
+![জ্যাক-ও-ল্যান্টার্নস](../../../translated_images/bn/jack-o-lanterns.181c661a9212457d.webp)
 > ছবি: <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> এর তোলা <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> এ
 
 ## আপনি কী শিখবেন
diff --git a/translations/bn/3-Web-App/README.md b/translations/bn/3-Web-App/README.md
index d8bb63e15..184ac2bfa 100644
--- a/translations/bn/3-Web-App/README.md
+++ b/translations/bn/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 এই পাঠ্যক্রমের এই অংশে, আপনি একটি প্রয়োগকৃত মেশিন লার্নিং বিষয়ের সাথে পরিচিত হবেন: কীভাবে আপনার Scikit-learn মডেলকে একটি ফাইলে সংরক্ষণ করবেন যা একটি ওয়েব অ্যাপ্লিকেশনের মধ্যে পূর্বাভাস দেওয়ার জন্য ব্যবহার করা যেতে পারে। মডেলটি সংরক্ষণ করার পরে, আপনি শিখবেন কীভাবে এটি Flask-এ তৈরি একটি ওয়েব অ্যাপে ব্যবহার করবেন। প্রথমে, আপনি কিছু ডেটা ব্যবহার করে একটি মডেল তৈরি করবেন যা UFO দেখার ঘটনার উপর ভিত্তি করে! এরপর, আপনি একটি ওয়েব অ্যাপ তৈরি করবেন যা আপনাকে সেকেন্ডের একটি সংখ্যা, একটি অক্ষাংশ এবং দ্রাঘিমাংশের মান ইনপুট দিয়ে পূর্বাভাস করতে দেবে কোন দেশ UFO দেখার রিপোর্ট করেছে।
 
-![UFO পার্কিং](../../../translated_images/bn/ufo.9e787f5161da9d4d.jpg)
+![UFO পার্কিং](../../../translated_images/bn/ufo.9e787f5161da9d4d.webp)
 
 ছবি তুলেছেন <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">মাইকেল হেরেন</a> <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">আনস্প্ল্যাশ</a>-এ
 
diff --git a/translations/bn/4-Classification/README.md b/translations/bn/4-Classification/README.md
index 7202e0978..b1ba32ffc 100644
--- a/translations/bn/4-Classification/README.md
+++ b/translations/bn/4-Classification/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 এশিয়া এবং ভারতে খাবারের ঐতিহ্য অত্যন্ত বৈচিত্র্যময় এবং খুবই সুস্বাদু! চলুন আঞ্চলিক খাবারের উপাদান সম্পর্কে বোঝার জন্য ডেটা দেখি।
 
-![থাই খাবার বিক্রেতা](../../../translated_images/bn/thai-food.c47a7a7f9f05c218.jpg)
+![থাই খাবার বিক্রেতা](../../../translated_images/bn/thai-food.c47a7a7f9f05c218.webp)
 > ছবি <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">লিশেং চ্যাং</a> এর তোলা <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">আনস্প্ল্যাশ</a> এ
   
 ## আপনি কী শিখবেন
diff --git a/translations/bn/5-Clustering/README.md b/translations/bn/5-Clustering/README.md
index b24dcc239..3717ec878 100644
--- a/translations/bn/5-Clustering/README.md
+++ b/translations/bn/5-Clustering/README.md
@@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 নাইজেরিয়ার বৈচিত্র্যময় শ্রোতাদের সঙ্গীতের রুচিও বৈচিত্র্যময়। Spotify থেকে সংগৃহীত ডেটা ব্যবহার করে (এই [প্রবন্ধটি](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421) দ্বারা অনুপ্রাণিত), আসুন নাইজেরিয়ায় জনপ্রিয় কিছু সঙ্গীত দেখি। এই ডেটাসেটে বিভিন্ন গানের 'danceability' স্কোর, 'acousticness', শব্দের উচ্চতা, 'speechiness', জনপ্রিয়তা এবং এনার্জি সম্পর্কিত তথ্য অন্তর্ভুক্ত রয়েছে। এই ডেটায় প্যাটার্ন খুঁজে বের করাটা বেশ মজার হবে!
 
-![একটি টার্নটেবিল](../../../translated_images/bn/turntable.f2b86b13c53302dc.jpg)
+![একটি টার্নটেবিল](../../../translated_images/bn/turntable.f2b86b13c53302dc.webp)
 
 > ছবি তুলেছেন <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>-এ
   
diff --git a/translations/bn/6-NLP/README.md b/translations/bn/6-NLP/README.md
index 1fefc263c..9d94aa829 100644
--- a/translations/bn/6-NLP/README.md
+++ b/translations/bn/6-NLP/README.md
@@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 এই পাঠগুলোতে আমরা ছোট ছোট কথোপকথনকারী বট তৈরি করে NLP-এর মৌলিক বিষয়গুলো শিখব, যাতে মেশিন লার্নিং কীভাবে এই কথোপকথনগুলোকে আরও 'স্মার্ট' করে তুলতে সাহায্য করে তা বোঝা যায়। আপনি সময়ের পিছনে ভ্রমণ করবেন, জেন অস্টেনের ক্লাসিক উপন্যাস **Pride and Prejudice**, যা ১৮১৩ সালে প্রকাশিত হয়েছিল, থেকে এলিজাবেথ বেনেট এবং মিস্টার ডার্সির সাথে কথা বলবেন। এরপর, ইউরোপের হোটেল রিভিউয়ের মাধ্যমে অনুভূতি বিশ্লেষণ সম্পর্কে শিখে আপনার জ্ঞান আরও বাড়াবেন।
 
-![Pride and Prejudice বই এবং চা](../../../translated_images/bn/p&p.279f1c49ecd88941.jpg)
+![Pride and Prejudice বই এবং চা](../../../translated_images/bn/p&p.279f1c49ecd88941.webp)
 > ছবি <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> এর তোলা <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> এ
   
 ## পাঠসমূহ
diff --git a/translations/bn/7-TimeSeries/README.md b/translations/bn/7-TimeSeries/README.md
index 9013a63d6..b32dcffd6 100644
--- a/translations/bn/7-TimeSeries/README.md
+++ b/translations/bn/7-TimeSeries/README.md
@@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 আমাদের আঞ্চলিক ফোকাস হলো বিশ্বব্যাপী বিদ্যুৎ ব্যবহার, যা একটি আকর্ষণীয় ডেটাসেট যা অতীতের লোডের প্যাটার্নের উপর ভিত্তি করে ভবিষ্যতের বিদ্যুৎ ব্যবহারের পূর্বাভাস সম্পর্কে শেখার সুযোগ দেয়। আপনি দেখতে পাবেন যে এই ধরনের পূর্বাভাস ব্যবসায়িক পরিবেশে কতটা কার্যকর হতে পারে।
 
-![electric grid](../../../translated_images/bn/electric-grid.0c21d5214db09ffa.jpg)
+![electric grid](../../../translated_images/bn/electric-grid.0c21d5214db09ffa.webp)
 
 ছবি [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) কর্তৃক, রাজস্থানের একটি রাস্তায় বৈদ্যুতিক টাওয়ারের। [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) থেকে নেওয়া।
 
diff --git a/translations/bn/8-Reinforcement/README.md b/translations/bn/8-Reinforcement/README.md
index ec2dd0021..25cf50e03 100644
--- a/translations/bn/8-Reinforcement/README.md
+++ b/translations/bn/8-Reinforcement/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 ধরুন আপনার কাছে একটি সিমুলেটেড পরিবেশ আছে, যেমন শেয়ার বাজার। যদি আপনি একটি নির্দিষ্ট নিয়ম আরোপ করেন, তাহলে কী ঘটে? এর ইতিবাচক বা নেতিবাচক প্রভাব কি? যদি কিছু নেতিবাচক ঘটে, তাহলে আপনাকে এই _নেতিবাচক রিইনফোর্সমেন্ট_ থেকে শিখতে হবে এবং পথ পরিবর্তন করতে হবে। যদি এটি ইতিবাচক ফলাফল হয়, তাহলে আপনাকে সেই _ইতিবাচক রিইনফোর্সমেন্ট_ এর উপর ভিত্তি করে এগিয়ে যেতে হবে।
 
-![পিটার এবং নেকড়ে](../../../translated_images/bn/peter.779730f9ba3a8a8d.png)
+![পিটার এবং নেকড়ে](../../../translated_images/bn/peter.779730f9ba3a8a8d.webp)
 
 > পিটার এবং তার বন্ধুরা ক্ষুধার্ত নেকড়ের হাত থেকে পালাতে হবে! ছবি: [জেন লুপার](https://twitter.com/jenlooper)
 
diff --git a/translations/bn/9-Real-World/README.md b/translations/bn/9-Real-World/README.md
index fa803c1fc..e8fe4e006 100644
--- a/translations/bn/9-Real-World/README.md
+++ b/translations/bn/9-Real-World/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 এই পাঠক্রমের এই অংশে, আপনাকে ক্লাসিক্যাল মেশিন লার্নিং-এর কিছু বাস্তব জীবনের প্রয়োগের সাথে পরিচয় করানো হবে। আমরা ইন্টারনেট থেকে সাদা কাগজ এবং প্রবন্ধ সংগ্রহ করেছি যেখানে এই কৌশলগুলো ব্যবহার করা হয়েছে, যতটা সম্ভব নিউরাল নেটওয়ার্ক, ডিপ লার্নিং এবং AI এড়িয়ে। শিখুন কীভাবে মেশিন লার্নিং ব্যবসায়িক সিস্টেম, পরিবেশগত প্রয়োগ, অর্থনীতি, শিল্প ও সংস্কৃতি এবং আরও অনেক ক্ষেত্রে ব্যবহার করা হয়।
 
-![chess](../../../translated_images/bn/chess.e704a268781bdad8.jpg)
+![chess](../../../translated_images/bn/chess.e704a268781bdad8.webp)
 
 > ছবি <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> এর তোলা <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> এ
   
diff --git a/translations/bn/README.md b/translations/bn/README.md
index c4ed3d954..431afaf77 100644
--- a/translations/bn/README.md
+++ b/translations/bn/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 আমাদের কাছে একটি Discord-ভিত্তিক "AI-এর সাথে শেখা" সিরিজ চলছে, আরো জানতে এবং আমাদের সঙ্গে যোগ দিতে যান [AI-এর সাথে শেখার সিরিজ](https://aka.ms/learnwithai/discord) এ, সময়কাল 18 - 30 সেপ্টেম্বর, 2025। আপনি GitHub Copilot ব্যবহার করে ডেটা সায়েন্সের জন্য টিপস এবং কৌশল শিখবেন।
 
-![AI-এর সাথে শেখার সিরিজ](../../translated_images/bn/3.9b58fd8d6c373c20.png)
+![AI-এর সাথে শেখার সিরিজ](../../translated_images/bn/3.9b58fd8d6c373c20.webp)
 
 # শুরুরদের জন্য মেশিন লার্নিং - একটি পাঠক্রম
 
@@ -80,7 +80,7 @@ Microsoft-এর Cloud Advocates একটি 12 সপ্তাহ, 26-উপ
 
 কিছু পাঠ শর্ট-ফর্ম ভিডিও হিসেবে উপলব্ধ। আপনি এগুলো পাঠের ভিতরে দেখতে পাবেন, অথবা নিচের চিত্রে ক্লিক করে [Microsoft Developer YouTube চ্যানেলের ML for Beginners প্লেলিস্টে](https://aka.ms/ml-beginners-videos) যেতে পারেন।
 
-[![ML for Beginners ব্যানার](../../translated_images/bn/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![ML for Beginners ব্যানার](../../translated_images/bn/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/br/1-Introduction/README.md b/translations/br/1-Introduction/README.md
index 997e4e6ed..340b76503 100644
--- a/translations/br/1-Introduction/README.md
+++ b/translations/br/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Nesta seção do currículo, você será apresentado aos conceitos básicos que fundamentam o campo do aprendizado de máquina, o que ele é, e aprenderá sobre sua história e as técnicas que os pesquisadores utilizam para trabalhar com ele. Vamos explorar juntos este novo mundo do aprendizado de máquina!
 
-![globo](../../../translated_images/br/globe.59f26379ceb40428.jpg)  
+![globo](../../../translated_images/br/globe.59f26379ceb40428.webp)  
 > Foto por <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> no <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 ### Aulas
diff --git a/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index 937de3a81..3f7fa9859 100644
--- a/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -48,7 +48,7 @@
     "   width=\"630\"/>\n",
     "   <figcaption>Arte por @allison_horst</figcaption>\n",
     "\n",
-    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/encouRage.e75d5fe0367fb913.jpg)<br>Arte por @allison_horst-->\n"
+    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/encouRage.e75d5fe0367fb913.webp)<br>Arte por @allison_horst-->\n"
    ],
    "metadata": {
     "id": "LWNNzfqd6feZ"
diff --git a/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index 4653e468d..cf706199e 100644
--- a/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -227,7 +227,7 @@
     "   <figcaption>Arte por @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/dplyr_wrangling.f5f99c64fd4580f1.png)<br/>Arte por \\@allison_horst-->\n"
+    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>Arte por \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "o4jLY5-VZO2C"
@@ -531,7 +531,7 @@
     "   <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/data-visualization.54e56dded7c1a804.png){width=\"600\"}-->\n",
+    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "Existe um *sábio* ditado que diz o seguinte:\n",
     "\n",
diff --git a/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 5e243e03c..0c5b3261c 100644
--- a/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -162,7 +162,7 @@
     "   <figcaption>Arte por @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/janitor.e4a77dd3d3e6a32e.jpg){width=\"700\"}-->\n"
+    "<!--![Arte por \\@allison_horst](../../../../../../translated_images/br/janitor.e4a77dd3d3e6a32e.webp){width=\"700\"}-->\n"
    ],
    "metadata": {
     "id": "WdUKXk7Bs8-V"
@@ -567,7 +567,7 @@
     "   <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "YqXjLuWavNxW"
@@ -808,7 +808,7 @@
     "   <figcaption>Infográfico por Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "HOCqJXLTwtWI"
diff --git a/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index c360c42b2..bfcb4e787 100644
--- a/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## Construir um modelo de regressão logística - Aula 4\n",
     "\n",
-    "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/br/linear-vs-logistic.ba180bf95e7ee667.png)\n",
+    "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/br/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[Questionário pré-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "A regressão logística não oferece os mesmos recursos que a regressão linear. A primeira fornece uma previsão sobre uma `categoria binária` (\"laranja ou não laranja\"), enquanto a segunda é capaz de prever `valores contínuos`, por exemplo, dado a origem de uma abóbora e o momento da colheita, *quanto o preço dela vai aumentar*.\n",
     "\n",
-    "![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/pumpkin-classifier.562771f104ad5436.png)\n",
+    "![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### Outras classificações\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **Ordinal**, que envolve categorias ordenadas, útil se quisermos organizar nossos resultados de forma lógica, como nossas abóboras ordenadas por um número finito de tamanhos (mini,pequeno,médio,grande,xl,xxl).\n",
     "\n",
-    "![Regressão multinomial vs ordinal](../../../../../../translated_images/br/multinomial-vs-ordinal.36701b4850e37d86.png)\n",
+    "![Regressão multinomial vs ordinal](../../../../../../translated_images/br/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **As variáveis NÃO precisam ser correlacionadas**\n",
     "\n",
diff --git a/translations/br/2-Regression/README.md b/translations/br/2-Regression/README.md
index 09218d631..5bccd587e 100644
--- a/translations/br/2-Regression/README.md
+++ b/translations/br/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Na América do Norte, as abóboras são frequentemente esculpidas em rostos assustadores para o Halloween. Vamos descobrir mais sobre esses vegetais fascinantes!
 
-![jack-o-lanterns](../../../translated_images/br/jack-o-lanterns.181c661a9212457d.jpg)
+![jack-o-lanterns](../../../translated_images/br/jack-o-lanterns.181c661a9212457d.webp)
 > Foto de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> no <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## O que você vai aprender
diff --git a/translations/br/3-Web-App/README.md b/translations/br/3-Web-App/README.md
index f722b5410..1757b539c 100644
--- a/translations/br/3-Web-App/README.md
+++ b/translations/br/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Nesta seção do currículo, você será introduzido a um tópico aplicado de aprendizado de máquina: como salvar seu modelo Scikit-learn como um arquivo que pode ser usado para fazer previsões dentro de um aplicativo web. Depois que o modelo estiver salvo, você aprenderá como utilizá-lo em um aplicativo web construído com Flask. Primeiro, você criará um modelo usando alguns dados relacionados a avistamentos de OVNIs! Em seguida, você construirá um aplicativo web que permitirá inserir um número de segundos junto com valores de latitude e longitude para prever qual país relatou ter visto um OVNI.
 
-![Estacionamento de OVNIs](../../../translated_images/br/ufo.9e787f5161da9d4d.jpg)
+![Estacionamento de OVNIs](../../../translated_images/br/ufo.9e787f5161da9d4d.webp)
 
 Foto por <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> no <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
diff --git a/translations/br/4-Classification/README.md b/translations/br/4-Classification/README.md
index ce6e7c784..fa008deb4 100644
--- a/translations/br/4-Classification/README.md
+++ b/translations/br/4-Classification/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Na Ásia e na Índia, as tradições culinárias são extremamente diversas e muito deliciosas! Vamos analisar dados sobre culinárias regionais para tentar entender seus ingredientes.
 
-![Vendedor de comida tailandesa](../../../translated_images/br/thai-food.c47a7a7f9f05c218.jpg)
+![Vendedor de comida tailandesa](../../../translated_images/br/thai-food.c47a7a7f9f05c218.webp)
 > Foto por <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> no <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## O que você vai aprender
diff --git a/translations/br/5-Clustering/README.md b/translations/br/5-Clustering/README.md
index b0e9eeb1c..3b2012f1a 100644
--- a/translations/br/5-Clustering/README.md
+++ b/translations/br/5-Clustering/README.md
@@ -15,7 +15,7 @@ Clustering é uma tarefa de aprendizado de máquina que busca encontrar objetos
 
 O público diversificado da Nigéria tem gostos musicais igualmente variados. Usando dados extraídos do Spotify (inspirado por [este artigo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), vamos analisar algumas músicas populares na Nigéria. Este conjunto de dados inclui informações sobre a pontuação de 'dançabilidade', 'acousticness', volume, 'speechiness', popularidade e energia de várias músicas. Será interessante descobrir padrões nesses dados!
 
-![Um toca-discos](../../../translated_images/br/turntable.f2b86b13c53302dc.jpg)
+![Um toca-discos](../../../translated_images/br/turntable.f2b86b13c53302dc.webp)
 
 > Foto por <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> no <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/br/6-NLP/README.md b/translations/br/6-NLP/README.md
index 06834f9e8..f7729dc48 100644
--- a/translations/br/6-NLP/README.md
+++ b/translations/br/6-NLP/README.md
@@ -17,7 +17,7 @@ Nesta seção do currículo, você será introduzido a um dos usos mais difundid
 
 Nestas lições, aprenderemos os fundamentos do PLN construindo pequenos bots conversacionais para entender como o aprendizado de máquina ajuda a tornar essas conversas cada vez mais 'inteligentes'. Você viajará no tempo, conversando com Elizabeth Bennett e Mr. Darcy do clássico romance de Jane Austen, **Orgulho e Preconceito**, publicado em 1813. Depois, você aprofundará seu conhecimento aprendendo sobre análise de sentimentos por meio de avaliações de hotéis na Europa.
 
-![Livro Orgulho e Preconceito e chá](../../../translated_images/br/p&p.279f1c49ecd88941.jpg)
+![Livro Orgulho e Preconceito e chá](../../../translated_images/br/p&p.279f1c49ecd88941.webp)
 > Foto por <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> no <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Lições
diff --git a/translations/br/7-TimeSeries/README.md b/translations/br/7-TimeSeries/README.md
index 357c057bb..57deae96b 100644
--- a/translations/br/7-TimeSeries/README.md
+++ b/translations/br/7-TimeSeries/README.md
@@ -17,7 +17,7 @@ Nestes dois módulos, você será introduzido à previsão de séries temporais,
 
 Nosso foco regional é o uso de eletricidade no mundo, um conjunto de dados interessante para aprender a prever o consumo futuro de energia com base em padrões de carga anteriores. Você verá como esse tipo de previsão pode ser extremamente útil em um ambiente empresarial.
 
-![rede elétrica](../../../translated_images/br/electric-grid.0c21d5214db09ffa.jpg)
+![rede elétrica](../../../translated_images/br/electric-grid.0c21d5214db09ffa.webp)
 
 Foto de [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) de torres elétricas em uma estrada em Rajasthan no [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
 
diff --git a/translations/br/8-Reinforcement/README.md b/translations/br/8-Reinforcement/README.md
index 3f61d6816..de8c90a4b 100644
--- a/translations/br/8-Reinforcement/README.md
+++ b/translations/br/8-Reinforcement/README.md
@@ -13,7 +13,7 @@ O aprendizado por reforço, RL, é considerado um dos paradigmas básicos de apr
 
 Imagine que você tem um ambiente simulado, como o mercado de ações. O que acontece se você impuser uma determinada regulamentação? Isso terá um efeito positivo ou negativo? Se algo negativo acontecer, você precisa aceitar esse _reforço negativo_, aprender com ele e mudar de direção. Se o resultado for positivo, você precisa construir sobre esse _reforço positivo_.
 
-![Pedro e o lobo](../../../translated_images/br/peter.779730f9ba3a8a8d.png)
+![Pedro e o lobo](../../../translated_images/br/peter.779730f9ba3a8a8d.webp)
 
 > Pedro e seus amigos precisam escapar do lobo faminto! Imagem por [Jen Looper](https://twitter.com/jenlooper)
 
diff --git a/translations/br/9-Real-World/README.md b/translations/br/9-Real-World/README.md
index f23816812..cc155d186 100644
--- a/translations/br/9-Real-World/README.md
+++ b/translations/br/9-Real-World/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Nesta seção do currículo, você será apresentado a algumas aplicações reais do aprendizado de máquina clássico. Pesquisamos na internet para encontrar artigos e publicações sobre aplicações que utilizam essas estratégias, evitando ao máximo redes neurais, aprendizado profundo e IA. Descubra como o aprendizado de máquina é usado em sistemas empresariais, aplicações ecológicas, finanças, artes e cultura, entre outros.
 
-![chess](../../../translated_images/br/chess.e704a268781bdad8.jpg)
+![chess](../../../translated_images/br/chess.e704a268781bdad8.webp)
 
 > Foto por <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> no <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/br/README.md b/translations/br/README.md
index 64bb5b926..9fcf193d7 100644
--- a/translations/br/README.md
+++ b/translations/br/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Temos uma série no Discord "Learn with AI" em andamento, saiba mais e junte-se a nós em [Série Aprenda com IA](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Você receberá dicas e truques de como usar o GitHub Copilot para Data Science.
 
-![Série Aprenda com IA](../../translated_images/br/3.9b58fd8d6c373c20.png)
+![Série Aprenda com IA](../../translated_images/br/3.9b58fd8d6c373c20.webp)
 
 # Aprendizado de Máquina para Iniciantes - Um Currículo
 
@@ -80,7 +80,7 @@ Siga estes passos:
 
 Algumas das lições estão disponíveis em vídeo de formato curto. Você pode encontrar todos eles incorporados nas lições, ou na [playlist ML for Beginners no canal Microsoft Developer no YouTube](https://aka.ms/ml-beginners-videos) clicando na imagem abaixo.
 
-[![Banner ML para iniciantes](../../translated_images/br/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![Banner ML para iniciantes](../../translated_images/br/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/cs/README.md b/translations/cs/README.md
index 244d73315..45273aa3f 100644
--- a/translations/cs/README.md
+++ b/translations/cs/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Probíhá naše série „Learn with AI“ na Discordu — dozvíte se více a připojte se k nám na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. září 2025. Získáte tipy a triky pro používání GitHub Copilot pro datovou vědu.
 
-![Série Learn with AI](../../translated_images/cs/3.9b58fd8d6c373c20.png)
+![Série Learn with AI](../../translated_images/cs/3.9b58fd8d6c373c20.webp)
 
 # Strojové učení pro začátečníky - osnovy kurzu
 
@@ -80,7 +80,7 @@ Postupujte podle těchto kroků:
 
 Některé lekce jsou dostupné jako krátká videa. Vše najdete vložené přímo v lekcích nebo v [playlistu ML for Beginners na kanálu Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) kliknutím na obrázek níže.
 
-[![Banner ML for beginners](../../translated_images/cs/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![Banner ML for beginners](../../translated_images/cs/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/da/README.md b/translations/da/README.md
index 290fc735e..90e9d15e3 100644
--- a/translations/da/README.md
+++ b/translations/da/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Vi har en løbende Discord "Learn with AI"-serie; læs mere og deltag hos [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og tricks til at bruge GitHub Copilot til Data Science.
 
-![Lær med AI-serien](../../translated_images/da/3.9b58fd8d6c373c20.png)
+![Lær med AI-serien](../../translated_images/da/3.9b58fd8d6c373c20.webp)
 
 # Maskinlæring for begyndere - En læseplan
 
@@ -80,7 +80,7 @@ Følg disse trin:
 
 Nogle af lektionerne er tilgængelige som kortformede videoer. Du kan finde dem indlejret i lektionerne, eller på [ML for Beginners-playlisten på Microsoft Developer YouTube-kanalen](https://aka.ms/ml-beginners-videos) ved at klikke på billedet nedenfor.
 
-[![ML for beginners-banner](../../translated_images/da/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners-banner](../../translated_images/da/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/de/1-Introduction/README.md b/translations/de/1-Introduction/README.md
index 8c14dde88..a88d39bd7 100644
--- a/translations/de/1-Introduction/README.md
+++ b/translations/de/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In diesem Abschnitt des Lehrplans werden Sie mit den grundlegenden Konzepten des maschinellen Lernens vertraut gemacht, erfahren, was es ist, und etwas über seine Geschichte sowie die Techniken lernen, die Forscher verwenden, um damit zu arbeiten. Lassen Sie uns diese neue Welt des maschinellen Lernens gemeinsam erkunden!
 
-![globe](../../../translated_images/de/globe.59f26379ceb40428.jpg)
+![globe](../../../translated_images/de/globe.59f26379ceb40428.webp)
 > Foto von <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> auf <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Lektionen
diff --git a/translations/de/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/de/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index 082ec576b..8480e841c 100644
--- a/translations/de/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/de/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -48,7 +48,7 @@
     "   width=\"630\"/>\n",
     "   <figcaption>Kunstwerk von @allison_horst</figcaption>\n",
     "\n",
-    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/encouRage.e75d5fe0367fb913.jpg)<br>Kunstwerk von @allison_horst-->\n"
+    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/encouRage.e75d5fe0367fb913.webp)<br>Kunstwerk von @allison_horst-->\n"
    ],
    "metadata": {
     "id": "LWNNzfqd6feZ"
diff --git a/translations/de/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/de/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index eb00bc99b..2e4c99351 100644
--- a/translations/de/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/de/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -49,7 +49,7 @@
     "   <figcaption>Kunstwerk von @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/unruly_data.0eedc7ced92d2d91.jpg)<br>Kunstwerk von \\@allison_horst-->\n"
+    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/unruly_data.0eedc7ced92d2d91.webp)<br>Kunstwerk von \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "Pg5aexcOPqAZ"
@@ -230,7 +230,7 @@
     "   <figcaption>Illustration von @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Illustration von \\@allison_horst](../../../../../../translated_images/de/dplyr_wrangling.f5f99c64fd4580f1.png)<br/>Illustration von \\@allison_horst-->\n"
+    "<!--![Illustration von \\@allison_horst](../../../../../../translated_images/de/dplyr_wrangling.f5f99c64fd4580f1.webp)<br/>Illustration von \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "o4jLY5-VZO2C"
@@ -536,7 +536,7 @@
     "   <figcaption>Infografik von Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/data-visualization.54e56dded7c1a804.png){width=\"600\"}-->\n",
+    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "Es gibt ein *weises* Sprichwort, das so lautet:\n",
     "\n",
diff --git a/translations/de/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/de/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index deacd9e05..3c1839940 100644
--- a/translations/de/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/de/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -162,7 +162,7 @@
     "   <figcaption>Kunstwerk von @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/janitor.e4a77dd3d3e6a32e.jpg){width=\"700\"}-->\n"
+    "<!--![Kunstwerk von \\@allison_horst](../../../../../../translated_images/de/janitor.e4a77dd3d3e6a32e.webp){width=\"700\"}-->\n"
    ],
    "metadata": {
     "id": "WdUKXk7Bs8-V"
@@ -567,7 +567,7 @@
     "   <figcaption>Infografik von Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "YqXjLuWavNxW"
@@ -808,7 +808,7 @@
     "   <figcaption>Infografik von Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![Infografik von Dasani Madipalli](../../../../../../translated_images/de/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "HOCqJXLTwtWI"
diff --git a/translations/de/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/de/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index bb3e92367..64098cbe4 100644
--- a/translations/de/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/de/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## Erstellen Sie ein logistisches Regressionsmodell - Lektion 4\n",
     "\n",
-    "![Infografik: Logistische vs. lineare Regression](../../../../../../translated_images/de/linear-vs-logistic.ba180bf95e7ee667.png)\n",
+    "![Infografik: Logistische vs. lineare Regression](../../../../../../translated_images/de/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[Quiz vor der Vorlesung](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "Die logistische Regression bietet nicht die gleichen Funktionen wie die lineare Regression. Erstere liefert eine Vorhersage über eine `binäre Kategorie` (\"orange oder nicht orange\"), während letztere in der Lage ist, `kontinuierliche Werte` vorherzusagen, zum Beispiel basierend auf der Herkunft eines Kürbisses und der Erntezeit, *wie stark sein Preis steigen wird*.\n",
     "\n",
-    "![Infografik von Dasani Madipalli](../../../../../../translated_images/de/pumpkin-classifier.562771f104ad5436.png)\n",
+    "![Infografik von Dasani Madipalli](../../../../../../translated_images/de/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### Andere Klassifikationen\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **Ordinal**, bei der geordnete Kategorien verwendet werden, nützlich, wenn wir unsere Ergebnisse logisch ordnen möchten, wie unsere Kürbisse, die nach einer begrenzten Anzahl von Größen geordnet sind (mini, sm, med, lg, xl, xxl).\n",
     "\n",
-    "![Multinomiale vs ordinale Regression](../../../../../../translated_images/de/multinomial-vs-ordinal.36701b4850e37d86.png)\n",
+    "![Multinomiale vs ordinale Regression](../../../../../../translated_images/de/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **Variablen MÜSSEN NICHT korrelieren**\n",
     "\n",
diff --git a/translations/de/2-Regression/README.md b/translations/de/2-Regression/README.md
index 1bffb1125..4cdcbc9bd 100644
--- a/translations/de/2-Regression/README.md
+++ b/translations/de/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In Nordamerika werden Kürbisse oft zu Halloween in gruselige Gesichter geschnitzt. Lassen Sie uns mehr über dieses faszinierende Gemüse herausfinden!
 
-![jack-o-lanterns](../../../translated_images/de/jack-o-lanterns.181c661a9212457d.jpg)
+![jack-o-lanterns](../../../translated_images/de/jack-o-lanterns.181c661a9212457d.webp)
 > Foto von <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> auf <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Was Sie lernen werden
diff --git a/translations/de/3-Web-App/README.md b/translations/de/3-Web-App/README.md
index 886592696..062110f18 100644
--- a/translations/de/3-Web-App/README.md
+++ b/translations/de/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In diesem Abschnitt des Lehrplans werden Sie mit einem angewandten ML-Thema vertraut gemacht: wie Sie Ihr Scikit-learn-Modell als Datei speichern können, die innerhalb einer Webanwendung für Vorhersagen verwendet werden kann. Sobald das Modell gespeichert ist, lernen Sie, wie Sie es in einer Web-App verwenden, die mit Flask erstellt wurde. Zunächst erstellen Sie ein Modell mit Daten, die sich mit UFO-Sichtungen befassen! Anschließend entwickeln Sie eine Web-App, die es ermöglicht, eine Anzahl von Sekunden zusammen mit einem Breitengrad- und Längengradwert einzugeben, um vorherzusagen, welches Land eine UFO-Sichtung gemeldet hat.
 
-![UFO-Parkplatz](../../../translated_images/de/ufo.9e787f5161da9d4d.jpg)
+![UFO-Parkplatz](../../../translated_images/de/ufo.9e787f5161da9d4d.webp)
 
 Foto von <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> auf <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
diff --git a/translations/de/4-Classification/README.md b/translations/de/4-Classification/README.md
index d89af27ad..b679a826f 100644
--- a/translations/de/4-Classification/README.md
+++ b/translations/de/4-Classification/README.md
@@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In Asien und Indien sind die Essensgewohnheiten äußerst vielfältig und sehr lecker! Schauen wir uns Daten über regionale Küchen an, um ihre Zutaten besser zu verstehen.
 
-![Thai-Essen-Verkäufer](../../../translated_images/de/thai-food.c47a7a7f9f05c218.jpg)
+![Thai-Essen-Verkäufer](../../../translated_images/de/thai-food.c47a7a7f9f05c218.webp)
 > Foto von <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> auf <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Was Sie lernen werden
diff --git a/translations/de/5-Clustering/README.md b/translations/de/5-Clustering/README.md
index 228165729..e4be77d80 100644
--- a/translations/de/5-Clustering/README.md
+++ b/translations/de/5-Clustering/README.md
@@ -15,7 +15,7 @@ Clustering ist eine Aufgabe des maschinellen Lernens, bei der versucht wird, Obj
 
 Das vielfältige Publikum in Nigeria hat ebenso vielfältige musikalische Vorlieben. Mithilfe von Daten, die von Spotify gesammelt wurden (inspiriert von [diesem Artikel](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), werfen wir einen Blick auf einige beliebte Musikstücke in Nigeria. Dieses Datenset enthält Informationen über verschiedene Songs, wie deren 'Danceability'-Score, 'Acousticness', Lautstärke, 'Speechiness', Popularität und Energie. Es wird spannend sein, Muster in diesen Daten zu entdecken!
 
-![Ein Plattenspieler](../../../translated_images/de/turntable.f2b86b13c53302dc.jpg)
+![Ein Plattenspieler](../../../translated_images/de/turntable.f2b86b13c53302dc.webp)
 
 > Foto von <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> auf <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/de/6-NLP/README.md b/translations/de/6-NLP/README.md
index 4388fb9d7..20663b4d9 100644
--- a/translations/de/6-NLP/README.md
+++ b/translations/de/6-NLP/README.md
@@ -17,7 +17,7 @@ In diesem Abschnitt des Lehrplans wirst du in eine der am weitesten verbreiteten
 
 In diesen Lektionen lernen wir die Grundlagen von NLP, indem wir kleine Konversationsbots erstellen, um zu verstehen, wie maschinelles Lernen dazu beiträgt, diese Gespräche immer „intelligenter“ zu machen. Du wirst in der Zeit zurückreisen und mit Elizabeth Bennett und Mr. Darcy aus Jane Austens klassischem Roman **Stolz und Vorurteil**, veröffentlicht im Jahr 1813, chatten. Anschließend vertiefst du dein Wissen, indem du mehr über Sentiment-Analyse anhand von Hotelbewertungen in Europa lernst.
 
-![Stolz und Vorurteil Buch und Tee](../../../translated_images/de/p&p.279f1c49ecd88941.jpg)
+![Stolz und Vorurteil Buch und Tee](../../../translated_images/de/p&p.279f1c49ecd88941.webp)
 > Foto von <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> auf <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Lektionen
diff --git a/translations/de/7-TimeSeries/README.md b/translations/de/7-TimeSeries/README.md
index f91bc0bee..a9f5db1f0 100644
--- a/translations/de/7-TimeSeries/README.md
+++ b/translations/de/7-TimeSeries/README.md
@@ -17,7 +17,7 @@ In diesen zwei Lektionen wirst du in die Zeitreihenprognose eingeführt, ein etw
 
 Unser regionaler Fokus liegt auf dem weltweiten Stromverbrauch, einem interessanten Datensatz, um zu lernen, wie man den zukünftigen Energieverbrauch basierend auf Mustern der vergangenen Last vorhersagen kann. Du wirst sehen, wie diese Art der Prognose in einem geschäftlichen Umfeld äußerst hilfreich sein kann.
 
-![Stromnetz](../../../translated_images/de/electric-grid.0c21d5214db09ffa.jpg)
+![Stromnetz](../../../translated_images/de/electric-grid.0c21d5214db09ffa.webp)
 
 Foto von [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) von Strommasten auf einer Straße in Rajasthan auf [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
 
diff --git a/translations/de/8-Reinforcement/README.md b/translations/de/8-Reinforcement/README.md
index 8abba1dc5..3f50e0600 100644
--- a/translations/de/8-Reinforcement/README.md
+++ b/translations/de/8-Reinforcement/README.md
@@ -13,7 +13,7 @@ Reinforcement Learning, RL, wird als eines der grundlegenden Paradigmen des masc
 
 Stellen Sie sich vor, Sie haben eine simulierte Umgebung wie den Aktienmarkt. Was passiert, wenn Sie eine bestimmte Regulierung einführen? Hat dies eine positive oder negative Wirkung? Wenn etwas Negatives passiert, müssen Sie diese _negative Verstärkung_ nutzen, daraus lernen und den Kurs ändern. Wenn es ein positives Ergebnis ist, müssen Sie darauf aufbauen und die _positive Verstärkung_ nutzen.
 
-![Peter und der Wolf](../../../translated_images/de/peter.779730f9ba3a8a8d.png)
+![Peter und der Wolf](../../../translated_images/de/peter.779730f9ba3a8a8d.webp)
 
 > Peter und seine Freunde müssen dem hungrigen Wolf entkommen! Bild von [Jen Looper](https://twitter.com/jenlooper)
 
diff --git a/translations/de/9-Real-World/README.md b/translations/de/9-Real-World/README.md
index ee564a234..9ae23d8d7 100644
--- a/translations/de/9-Real-World/README.md
+++ b/translations/de/9-Real-World/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 In diesem Abschnitt des Lehrplans werden Sie mit einigen realen Anwendungen des klassischen maschinellen Lernens vertraut gemacht. Wir haben das Internet durchforstet, um Whitepapers und Artikel über Anwendungen zu finden, die diese Strategien genutzt haben, und dabei neuralen Netzwerken, Deep Learning und KI so weit wie möglich vermieden. Erfahren Sie, wie maschinelles Lernen in Geschäftssystemen, ökologischen Anwendungen, Finanzen, Kunst und Kultur und mehr eingesetzt wird.
 
-![chess](../../../translated_images/de/chess.e704a268781bdad8.jpg)
+![chess](../../../translated_images/de/chess.e704a268781bdad8.webp)
 
 > Foto von <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> auf <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
diff --git a/translations/de/README.md b/translations/de/README.md
index 7d010efac..0251f6c41 100644
--- a/translations/de/README.md
+++ b/translations/de/README.md
@@ -1,8 +1,8 @@
 <!--
 CO_OP_TRANSLATOR_METADATA:
 {
-  "original_hash": "ec8385130a8239ad9d827175126a0722",
-  "translation_date": "2026-01-01T09:59:32+00:00",
+  "original_hash": "0a6f4476a4f3934a4aa47c1bf47158bc",
+  "translation_date": "2026-01-16T08:07:29+00:00",
   "source_file": "README.md",
   "language_code": "de"
 }
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 [![GitHub-Mitwirkende](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)
+[![PRs Willkommen](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
 [![GitHub-Beobachter](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/)
@@ -19,178 +19,187 @@ CO_OP_TRANSLATOR_METADATA:
 
 ### 🌐 Mehrsprachige Unterstützung
 
-#### Unterstützt durch GitHub Action (Automatisiert & immer aktuell)
+#### Unterstützt durch GitHub Action (Automatisiert & Immer Aktuell)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../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](./README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
+[Arabisch](../ar/README.md) | [Bengalisch](../bn/README.md) | [Bulgarisch](../bg/README.md) | [Birmanisch (Myanmar)](../my/README.md) | [Chinesisch (Vereinfacht)](../zh/README.md) | [Chinesisch (Traditionell, Hongkong)](../hk/README.md) | [Chinesisch (Traditionell, Macau)](../mo/README.md) | [Chinesisch (Traditionell, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tschechisch](../cs/README.md) | [Dänisch](../da/README.md) | [Niederländisch](../nl/README.md) | [Estnisch](../et/README.md) | [Finnisch](../fi/README.md) | [Französisch](../fr/README.md) | [Deutsch](./README.md) | [Griechisch](../el/README.md) | [Hebräisch](../he/README.md) | [Hindi](../hi/README.md) | [Ungarisch](../hu/README.md) | [Indonesisch](../id/README.md) | [Italienisch](../it/README.md) | [Japanisch](../ja/README.md) | [Kannada](../kn/README.md) | [Koreanisch](../ko/README.md) | [Litauisch](../lt/README.md) | [Malaiisch](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalesisch](../ne/README.md) | [Nigerianisches Pidgin](../pcm/README.md) | [Norwegisch](../no/README.md) | [Persisch (Farsi)](../fa/README.md) | [Polnisch](../pl/README.md) | [Portugiesisch (Brasilien)](../br/README.md) | [Portugiesisch (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumänisch](../ro/README.md) | [Russisch](../ru/README.md) | [Serbisch (Kyrillisch)](../sr/README.md) | [Slowakisch](../sk/README.md) | [Slowenisch](../sl/README.md) | [Spanisch](../es/README.md) | [Swahili](../sw/README.md) | [Schwedisch](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamilisch](../ta/README.md) | [Telugu](../te/README.md) | [Thailändisch](../th/README.md) | [Türkisch](../tr/README.md) | [Ukrainisch](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisch](../vi/README.md)
+
+> **Lieber lokal klonen?**
+
+> Dieses Repository enthält über 50 Sprachübersetzungen, welche die Download-Größe erheblich erhöhen. Um ohne Übersetzungen zu klonen, verwenden Sie Sparse Checkout:
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git
+> cd ML-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> Damit erhalten Sie alles, was Sie für den Kurs benötigen, mit einem viel schnelleren Download.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
 #### Treten Sie unserer Community bei
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Wir veranstalten eine Discord-Reihe "Learn with AI", erfahren Sie mehr und machen Sie mit unter [Learn with AI Series](https://aka.ms/learnwithai/discord) vom 18. bis 30. September 2025. Sie erhalten Tipps und Tricks zur Verwendung von GitHub Copilot für Data Science.
+Wir haben eine laufende Discord-Lernreihe mit AI, erfahren Sie mehr und schließen Sie sich uns an unter [Learn with AI Series](https://aka.ms/learnwithai/discord) vom 18. bis 30. September 2025. Sie erhalten Tipps und Tricks zur Nutzung von GitHub Copilot für Data Science.
 
-![Learn with AI-Serie](../../translated_images/de/3.9b58fd8d6c373c20.png)
+![Learn with AI series](../../../../translated_images/de/3.9b58fd8d6c373c20.webp)
 
-# Maschinelles Lernen für Einsteiger - Ein Lehrplan
+# Machine Learning für Einsteiger – Ein Lehrplan
 
 > 🌍 Reisen Sie um die Welt, während wir Maschinelles Lernen anhand von Weltkulturen erkunden 🌍
 
-Die Cloud Advocates bei Microsoft freuen sich, einen 12-wöchigen, 26-teiligen Lehrplan rund um das Thema **Maschinelles Lernen** anzubieten. In diesem Lehrplan lernen Sie das, was man manchmal als **klassisches Machine Learning** bezeichnet, hauptsächlich unter Verwendung von Scikit-learn als Bibliothek; Deep Learning wird in unserem [AI for Beginners' curriculum](https://aka.ms/ai4beginners) behandelt. Kombinieren Sie diese Lektionen auch mit unserem ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners)!
+Cloud Advocates bei Microsoft freuen sich, einen 12-wöchigen Lehrplan mit 26 Lektionen rund um das Thema **Maschinelles Lernen** anzubieten. In diesem Lehrplan lernen Sie, was manchmal als **klassisches maschinelles Lernen** bezeichnet wird, wobei hauptsächlich Scikit-learn als Bibliothek verwendet wird und Deep Learning ausgelassen wird, das in unserem [AI für Einsteiger Lehrplan](https://aka.ms/ai4beginners) behandelt wird. Kombinieren Sie diese Lektionen auch mit unserem ['Data Science für Einsteiger' Lehrplan](https://aka.ms/ds4beginners)!
 
-Reisen Sie mit uns um die Welt, während wir diese klassischen Techniken auf Daten aus vielen Regionen anwenden. Jede Lektion enthält Vor- und Nach-Quizze, schriftliche Anleitungen zur Durchführung der Lektion, eine Lösung, eine Aufgabe und mehr. Unsere projektbasierte Didaktik ermöglicht Lernen durch Bauen — eine bewährte Methode, damit neue Fähigkeiten "haften bleiben".
+Reisen Sie mit uns um die Welt, während wir diese klassischen Techniken auf Daten aus vielen Regionen anwenden. Jede Lektion umfasst Vor- und Nach-Kurs-Quizze, schriftliche Anleitungen zur Durchführung der Lektion, Lösungen, Aufgaben und mehr. Unsere projektbasierte Didaktik ermöglicht es Ihnen, durch das Erstellen zu lernen – eine bewährte Methode, um neue Fähigkeiten nachhaltig zu verankern.
 
-**✍️ Herzlichen Dank an unsere Autorinnen und Autoren** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu und Amy Boyd
+**✍️ Herzlichen Dank an unsere Autoren** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu und Amy Boyd
 
-**🎨 Ebenfalls Dank an unsere Illustratoren** Tomomi Imura, Dasani Madipalli und Jen Looper
+**🎨 Dank auch an unsere Illustratoren** Tomomi Imura, Dasani Madipalli und Jen Looper
 
-**🙏 Besonderer Dank 🙏 an unsere Microsoft Student Ambassador-Autorinnen und -Autoren, Reviewer und Inhaltsbeiträger**, namentlich Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila und Snigdha Agarwal
+**🙏 Besonderer Dank 🙏 an unsere Microsoft Student Ambassador Autoren, Gutachter und Inhaltsbeiträge**, insbesondere Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila und Snigdha Agarwal
 
-**🤩 Extra-Dank an die Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi und Vidushi Gupta für unsere R-Lektionen!**
+**🤩 Extra-Dank an Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi und Vidushi Gupta für unsere R-Lektionen!**
 
 # Erste Schritte
 
-Befolgen Sie diese Schritte:
-1. **Forken Sie das Repository**: Klicken Sie auf die Schaltfläche "Fork" oben rechts auf dieser Seite.
-2. **Klonen Sie das Repository**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
+Folgen Sie diesen Schritten:
+1. **Repository forken**: Klicken Sie oben rechts auf dieser Seite auf die Schaltfläche "Fork".
+2. **Repository klonen**:   `git clone https://github.com/microsoft/ML-For-Beginners.git`
 
-> [finden Sie alle zusätzlichen Ressourcen für diesen Kurs in unserer Microsoft Learn-Sammlung](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+> [Finden Sie alle zusätzlichen Ressourcen für diesen Kurs in unserer Microsoft Learn Sammlung](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
-> 🔧 **Brauchen Sie Hilfe?** Sehen Sie in unserer [Fehlerbehebungsanleitung](TROUBLESHOOTING.md) nach Lösungen für häufige Probleme bei Installation, Einrichtung und dem Ausführen von Lektionen.
+> 🔧 **Brauchen Sie Hilfe?** Sehen Sie in unserem [Troubleshooting Guide](TROUBLESHOOTING.md) nach Lösungen zu häufigen Problemen bei Installation, Einrichtung und Durchführung von Lektionen.
 
 
-**[Studierende](https://aka.ms/student-page)**, um dieses Curriculum zu nutzen, forken Sie das gesamte Repo in Ihr eigenes GitHub-Konto und bearbeiten Sie die Übungen allein oder in einer Gruppe:
+**[Studenten](https://aka.ms/student-page)**, um diesen Lehrplan zu nutzen, forken Sie das gesamte Repo auf Ihr eigenes GitHub-Konto und bearbeiten Sie die Übungen selbstständig oder in der Gruppe:
 
-- Beginnen Sie mit einem Quiz vor der Vorlesung.
-- Lesen Sie die Vorlesung und führen Sie die Aktivitäten durch, halten Sie bei jeder Wissensüberprüfung inne und reflektieren Sie.
-- Versuchen Sie, die Projekte durch das Verständnis der Lektionen zu erstellen, anstatt den Lösungscode auszuführen; dieser Code ist jedoch in den `/solution`-Ordnern jeder projektorientierten Lektion verfügbar.
-- Machen Sie das Quiz nach der Vorlesung.
-- Schließen Sie die Challenge ab.
-- Bearbeiten Sie die Aufgabe.
-- Nach Abschluss einer Lektionsgruppe besuchen Sie das [Diskussionsforum](https://github.com/microsoft/ML-For-Beginners/discussions) und "lernen Sie laut", indem Sie die entsprechende PAT-Rubrik ausfüllen. Ein 'PAT' ist ein Progress Assessment Tool, eine Bewertungsrubrik, die Sie ausfüllen, um Ihr Lernen zu fördern. Sie können auch auf andere PATs reagieren, damit wir gemeinsam lernen.
+- Beginnen Sie mit einem Vorlesungs-Quiz.
+- Lesen Sie die Lektion und absolvieren Sie die Aktivitäten mit Pausen und Reflexion bei jedem Wissenscheck.
+- Versuchen Sie, die Projekte eigenständig zu erstellen, indem Sie die Lektionen verstehen, statt nur den Lösungscode auszuführen; dieser Code ist allerdings in den `/solution`-Ordnern in jeder projekorientierten Lektion verfügbar.
+- Machen Sie das Nachvorlesungs-Quiz.
+- Absolvieren Sie die Challenge.
+- Erledigen Sie die Hausaufgabe.
+- Nach Beendigung einer Lektionengruppe besuchen Sie das [Diskussionsforum](https://github.com/microsoft/ML-For-Beginners/discussions) und „lernen laut“, indem Sie das passende PAT-Rubrikformular ausfüllen. Ein 'PAT' ist ein Progress Assessment Tool, eine Rubrik, die Sie ausfüllen, um Ihr Lernen zu vertiefen. Sie können auch auf andere PATs reagieren, so lernen wir gemeinsam.
 
-> Für weiterführendes Studium empfehlen wir das Durcharbeiten dieser [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) Module und Lernpfade.
+> Für weiterführendes Lernen empfehlen wir diese [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott) Module und Lernpfade.
 
-**Lehrkräfte**, wir haben [einige Vorschläge](for-teachers.md) zur Nutzung dieses Curriculums beigefügt.
+**Lehrer**, wir haben [einige Vorschläge](for-teachers.md) zur Nutzung dieses Lehrplans aufgenommen.
 
 ---
 
-## Video-Anleitungen
+## Videoanleitungen
 
-Einige Lektionen sind als Kurzvideos verfügbar. Sie können alle diese inline in den Lektionen finden oder auf der [ML for Beginners-Playlist auf dem Microsoft Developer YouTube-Kanal](https://aka.ms/ml-beginners-videos), indem Sie auf das Bild unten klicken.
+Einige Lektionen sind als Kurzvideos verfügbar. Diese finden Sie eingebettet in den Lektionen oder auf der [ML for Beginners-Playlist auf dem Microsoft Developer YouTube-Kanal](https://aka.ms/ml-beginners-videos) durch Klicken auf das Bild unten.
 
-[![ML for beginners Banner](../../translated_images/de/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![ML for beginners banner](../../../../translated_images/de/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
-## Treffen Sie das Team
+## Lernen Sie das Team kennen
 
 [![Promo-Video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU)
 
 **Gif von** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
 
-> 🎥 Klicken Sie auf das obige Bild für ein Video über das Projekt und die Personen, die es erstellt haben!
+> 🎥 Klicken Sie oben auf das Bild für ein Video über das Projekt und die Leute, die es erstellt haben!
 
 ---
 
-## Pädagogik
+## Didaktik
 
-Wir haben beim Aufbau dieses Lehrplans zwei pädagogische Grundsätze gewählt: sicherstellen, dass er praxisnah **projektbasiert** ist und dass er **häufige Quizze** enthält. Darüber hinaus hat dieser Lehrplan ein gemeinsames **Thema**, das ihm Kohärenz verleiht.
+Wir haben beim Aufbau dieses Lehrplans zwei pädagogische Grundsätze gewählt: sicherzustellen, dass er praxisnah **projektbasiert** ist und **häufige Quiz** enthält. Außerdem hat dieser Lehrplan ein gemeinsames **Thema** für Kohärenz.
 
-Indem die Inhalte an Projekte angelehnt sind, wird der Prozess für Lernende engagierender und das Behalten von Konzepten wird gesteigert. Ein niedrigschwelliges Quiz vor einer Unterrichtseinheit setzt zudem die Lernintention der Teilnehmenden, während ein zweites Quiz nach der Einheit die weitere Behaltensleistung sichert. Dieser Lehrplan wurde so gestaltet, dass er flexibel und unterhaltsam ist und vollständig oder teilweise durchlaufen werden kann. Die Projekte beginnen klein und werden bis zum Ende des 12-wöchigen Zyklus zunehmend komplexer. Dieser Lehrplan enthält außerdem ein Nachwort zu realen Anwendungen von ML, das als Bonuspunkte oder als Grundlage für Diskussionen genutzt werden kann.
+Indem der Inhalt auf Projekte ausgerichtet ist, wird der Prozess für Studierende ansprechender und die Begriffsvermittlung vertieft. Ein niedrigschwelliges Quiz vor einer Lektion richtet die Lernabsicht der Teilnehmer auf das Thema aus, während ein zweites Quiz nach der Lektion zusätzliche Festigung ermöglicht. Dieser Lehrplan ist flexibel und macht Spaß und kann ganz oder teilweise durchlaufen werden. Die Projekte beginnen einfach und werden im Verlauf des 12-Wochen-Zyklus zunehmend komplexer. Der Lehrplan enthält außerdem ein Nachwort zu realweltlichen Anwendungen von ML, welches als Zusatzaufgabe oder Diskussionsgrundlage genutzt werden kann.
 
-> Finden Sie unseren [Verhaltenskodex](CODE_OF_CONDUCT.md), [Beitragen](CONTRIBUTING.md), [Übersetzung](TRANSLATIONS.md) und [Fehlerbehebung](TROUBLESHOOTING.md) Leitfäden. Wir freuen uns über Ihr konstruktives Feedback!
+> Unseren [Verhaltenskodex](CODE_OF_CONDUCT.md), [Beitragsleitfaden](CONTRIBUTING.md), [Übersetzungsleitfaden](TRANSLATIONS.md) und [Problemlösungsanleitung](TROUBLESHOOTING.md) finden Sie hier. Wir freuen uns auf Ihr konstruktives Feedback!
 
-## Jede Lektion enthält
+## Jede Lektion beinhaltet
 
 - optionale Sketchnote
 - optionales ergänzendes Video
 - Videoanleitung (nur einige Lektionen)
-- [Aufwärmquiz vor der Vorlesung](https://ff-quizzes.netlify.app/en/ml/)
+- [Vorlesungs-Warmup-Quiz](https://ff-quizzes.netlify.app/en/ml/)
 - schriftliche Lektion
-- für projektbasierte Lektionen schrittweise Anleitungen zum Aufbau des Projekts
-- Wissenschecks
+- bei projektbasierten Lektionen Schritt-für-Schritt-Anleitungen zum Bau des Projekts
+- Wissens-Checks
 - eine Herausforderung
 - ergänzende Lektüre
 - Aufgabe
-- [Quiz nach der Vorlesung](https://ff-quizzes.netlify.app/en/ml/)
-
-> **Eine Anmerkung zu Programmiersprachen**: Diese Lektionen sind hauptsächlich in Python geschrieben, aber viele sind auch in R verfügbar. Um eine R-Lektion abzuschließen, gehen Sie in den `/solution`-Ordner und suchen Sie nach R-Lektionen. Diese enthalten eine .rmd-Erweiterung, die eine **R Markdown**-Datei darstellt, die einfach als Einbettung von `code chunks` (von R oder anderen Sprachen) und einem `YAML header` (der steuert, wie Ausgaben wie PDF formatiert werden) in einem `Markdown document` definiert werden kann. Als solches dient sie als beispielhaftes Erstellungsframework für Data Science, da sie es Ihnen ermöglicht, Ihren Code, dessen Ausgabe und Ihre Gedanken zu kombinieren, indem Sie diese in Markdown niederschreiben. Darüber hinaus können R Markdown-Dokumente in Ausgabedateiformate wie PDF, HTML oder Word gerendert werden.
-
-> **Eine Anmerkung zu Quizzen**: Alle Quizze sind im [Quiz-App-Ordner](../../quiz-app) enthalten, insgesamt 52 Quizze mit jeweils drei Fragen. Sie sind innerhalb der Lektionen verlinkt, aber die Quiz-App kann lokal ausgeführt werden; befolgen Sie die Anweisungen im `quiz-app`-Ordner, um lokal zu hosten oder auf Azure bereitzustellen.
-
-| Lektionsnummer |                             Thema                              |                   Lektionsgruppe                   | Lernziele                                                                                                             |                                                              Verknüpfte Lektion                                                               |                        Autor                        |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
-|      01       |                Einführung in maschinelles Lernen                |      [Einführung](1-Introduction/README.md)       | Lernen Sie die grundlegenden Konzepte des maschinellen Lernens                                                                                |                                             [Lektion](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                       |
-|      02       |                Die Geschichte des maschinellen Lernens                 |      [Einführung](1-Introduction/README.md)       | Erfahren Sie die Geschichte dieses Fachgebiets                                                                                         |                                            [Lektion](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen and Amy                      |
-|      03       |                 Fairness und maschinelles Lernen                  |      [Einführung](1-Introduction/README.md)       | Welche wichtigen philosophischen Fragen zur Fairness sollten Lernende beachten, wenn sie ML-Modelle erstellen und anwenden? |                                              [Lektion](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                        |
-|      04       |                Techniken des maschinellen Lernens                 |      [Einführung](1-Introduction/README.md)       | Welche Techniken verwenden ML-Forschende, um ML-Modelle zu erstellen?                                                                       |                                          [Lektion](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris and Jen                     |
-|      05       |                   Einführung in die Regression                   |        [Regression](2-Regression/README.md)         | Einstieg in Python und Scikit-learn für Regressionsmodelle                                                                  |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
-|      06       |                Nordamerikanische Kürbisspreise 🎃                |        [Regression](2-Regression/README.md)         | Daten visualisieren und bereinigen zur Vorbereitung auf ML                                                                                  |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
-|      07       |                Nordamerikanische Kürbisspreise 🎃                |        [Regression](2-Regression/README.md)         | Lineare und polynomiale Regressionsmodelle erstellen                                                                                   |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen and Dmitry • Eric Wanjau       |
-|      08       |                Nordamerikanische Kürbisspreise 🎃                |        [Regression](2-Regression/README.md)         | Ein logistisches Regressionsmodell erstellen                                                                                               |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
-|      09       |                          Eine Web-App 🔌                          |           [Web-App](3-Web-App/README.md)            | Erstellen Sie eine Web-App, um Ihr trainiertes Modell zu verwenden                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
-|      10       |                 Einführung in die Klassifikation                 |    [Klassifikation](4-Classification/README.md)     | Bereinigen, vorbereiten und visualisieren Sie Ihre Daten; Einführung in die Klassifikation                                                            | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen and Cassie • Eric Wanjau |
-|      11       |             Leckere asiatische und indische Küche 🍜             |    [Klassifikation](4-Classification/README.md)     | Einführung in Klassifikatoren                                                                                                     | [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       |             Leckere asiatische und indische Küche 🍜             |    [Klassifikation](4-Classification/README.md)     | Weitere Klassifikatoren                                                                                                                | [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       |             Leckere asiatische und indische Küche 🍜             |    [Klassifikation](4-Classification/README.md)     | Erstellen Sie eine Empfehlungs-Web-App mit Ihrem Modell                                                                                    |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                          |
-|      14       |                   Einführung in Clustering                   |        [Clustering](5-Clustering/README.md)         | Bereinigen, vorbereiten und visualisieren Sie Ihre Daten; Einführung in Clustering                                                                |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
-|      15       |              Nigerianische Musikgeschmäcker erkunden 🎧              |        [Clustering](5-Clustering/README.md)         | Erkunden Sie die K-Means-Clustering-Methode                                                                                           |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
-|      16       |        Einführung in die natürliche Sprachverarbeitung ☕️         |   [Natürliche Sprachverarbeitung](6-NLP/README.md)    | Lernen Sie die Grundlagen der NLP, indem Sie einen einfachen Bot erstellen                                                                             |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                        |
-|      17       |                      Häufige NLP-Aufgaben ☕️                      |   [Natürliche Sprachverarbeitung](6-NLP/README.md)    | Vertiefen Sie Ihr NLP-Wissen, indem Sie häufige Aufgaben verstehen, die beim Umgang mit Sprachstrukturen erforderlich sind                          |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                        |
-|      18       |             Übersetzung und Sentimentanalyse ♥️              |   [Natürliche Sprachverarbeitung](6-NLP/README.md)    | Übersetzung und Sentimentanalyse mit Jane Austen                                                                             |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                        |
-|      19       |                  Romantische Hotels Europas ♥️                  |   [Natürliche Sprachverarbeitung](6-NLP/README.md)    | Sentimentanalyse mit Hotelbewertungen 1                                                                                         |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                        |
-|      20       |                  Romantische Hotels Europas ♥️                  |   [Natürliche Sprachverarbeitung](6-NLP/README.md)    | Sentimentanalyse mit Hotelbewertungen 2                                                                                         |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                        |
-|      21       |            Einführung in Zeitreihenprognosen             |        [Zeitreihen](7-TimeSeries/README.md)        | Einführung in Zeitreihenprognosen                                                                                         |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                       |
-|      22       | ⚡️ Weltweiter Stromverbrauch ⚡️ - Zeitreihenprognosen mit ARIMA |        [Zeitreihen](7-TimeSeries/README.md)        | Zeitreihenprognosen mit ARIMA                                                                                              |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                       |
-|      23       |  ⚡️ Weltweiter Stromverbrauch ⚡️ - Zeitreihenprognosen mit SVR  |        [Zeitreihen](7-TimeSeries/README.md)        | Zeitreihenprognosen mit Support Vector Regressor                                                                           |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                        |
-|      24       |             Einführung in Reinforcement Learning             | [Reinforcement learning](8-Reinforcement/README.md) | Einführung in Reinforcement Learning mit Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
-|      25       |                 Hilf Peter, dem Wolf zu entkommen! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement-Learning-Gym                                                                                                      |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                        |
-|  Postscript   |            Echtwelt-ML-Szenarien und Anwendungen            |      [ML in the Wild](9-Real-World/README.md)       | Interessante und aufschlussreiche Anwendungsfälle klassischer ML                                                               |                                             [Lektion](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
-|  Postscript   |            Modell-Debugging im ML mit dem RAI-Dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Modell-Debugging im Machine Learning mit Komponenten des Responsible AI-Dashboards                                                              |                                             [Lektion](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
-
-> [finden Sie alle zusätzlichen Ressourcen für diesen Kurs in unserer Microsoft Learn-Sammlung](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+- [Nachvorlesungs-Quiz](https://ff-quizzes.netlify.app/en/ml/)
+
+> **Ein Hinweis zu den Sprachen**: Diese Lektionen sind primär in Python geschrieben, viele auch in R verfügbar. Um eine R-Lektion abzuschließen, gehen Sie in den `/solution`-Ordner und suchen Sie nach R-Lektionen. Diese haben die Dateiendung .rmd, was für **R Markdown** steht – ein Format, das `Code-Chunks` (von R oder anderen Sprachen) und einen `YAML-Header` (für Formatierungsanweisungen, z. B. PDF-Ausgabe) innerhalb eines `Markdown-Dokuments` kombiniert. Damit dient es als gutes Framework zur Dokumentation in Data Science, weil es erlaubt, Code, Ausgaben und Erklärungen in Markdown niederzuschreiben. R Markdown-Dokumente können in Formate wie PDF, HTML oder Word gerendert werden.
+> **Eine Anmerkung zu Quizzen**: Alle Quizze befinden sich im [Quiz App Ordner](../../quiz-app) und umfassen insgesamt 52 Quizze mit je drei Fragen. Sie sind aus den Lektionen verlinkt, aber die Quiz-App kann auch lokal ausgeführt werden; folgen Sie den Anweisungen im `quiz-app`-Ordner, um sie lokal zu hosten oder auf Azure bereitzustellen.
+
+| Lektion Nummer |                             Thema                              |                   Lektion Gruppierung                   | Lernziele                                                                                                                      |                                                              Verknüpfte Lektion                                                               |                        Autor                        |
+| :------------: | :------------------------------------------------------------: | :-----------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: |
+|      01        |                Einführung in das maschinelle Lernen           |      [Einführung](1-Introduction/README.md)             | Lernen Sie die grundlegenden Konzepte hinter dem maschinellen Lernen                                                             |                                             [Lektion](1-Introduction/1-intro-to-ML/README.md)                                             |                       Muhammad                     |
+|      02        |                Die Geschichte des maschinellen Lernens        |      [Einführung](1-Introduction/README.md)             | Lernen Sie die Geschichte dieses Fachgebiets                                                                                   |                                            [Lektion](1-Introduction/2-history-of-ML/README.md)                                            |                     Jen und Amy                    |
+|      03        |                 Gerechtigkeit und maschinelles Lernen         |      [Einführung](1-Introduction/README.md)             | Was sind die wichtigen philosophischen Fragen zur Gerechtigkeit, die Studierende beim Erstellen und Anwenden von ML-Modellen beachten sollten? |                                              [Lektion](1-Introduction/3-fairness/README.md)                                               |                        Tomomi                      |
+|      04        |                Techniken für maschinelles Lernen              |      [Einführung](1-Introduction/README.md)             | Welche Techniken verwenden ML-Forscher, um ML-Modelle zu erstellen?                                                             |                                          [Lektion](1-Introduction/4-techniques-of-ML/README.md)                                           |                    Chris und Jen                   |
+|      05        |                   Einführung in die Regression                |        [Regression](2-Regression/README.md)              | Einstieg in Python und Scikit-learn für Regressionsmodelle                                                                      |         [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html)         |      Jen • Eric Wanjau       |
+|      06        |                Nordamerikanische Kürbnispreise 🎃             |        [Regression](2-Regression/README.md)              | Visualisieren und Säubern von Daten zur Vorbereitung auf ML                                                                      |          [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html)          |      Jen • Eric Wanjau       |
+|      07        |                Nordamerikanische Kürbnispreise 🎃             |        [Regression](2-Regression/README.md)              | Erstellen linearer und polynomieller Regressionsmodelle                                                                         |        [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html)        |      Jen und Dmitry • Eric Wanjau       |
+|      08        |                Nordamerikanische Kürbnispreise 🎃             |        [Regression](2-Regression/README.md)              | Erstellen eines logistischen Regressionsmodells                                                                                 |     [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html)      |      Jen • Eric Wanjau       |
+|      09        |                          Eine Web-App 🔌                      |           [Web App](3-Web-App/README.md)                 | Erstellen Sie eine Web-App, um Ihr trainiertes Modell zu verwenden                                                               |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                        |
+|      10        |                 Einführung in die Klassifizierung             |    [Klassifizierung](4-Classification/README.md)        | Daten bereinigen, vorbereiten und visualisieren; Einführung in die Klassifizierung                                               | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html)  | Jen und Cassie • Eric Wanjau |
+|      11        |             Köstliche asiatische und indische Küchen 🍜       |    [Klassifizierung](4-Classification/README.md)        | Einführung in Klassifizierer                                                                                                    | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen und Cassie • Eric Wanjau |
+|      12        |             Köstliche asiatische und indische Küchen 🍜       |    [Klassifizierung](4-Classification/README.md)        | Weitere Klassifizierer                                                                                                          | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen und Cassie • Eric Wanjau |
+|      13        |             Köstliche asiatische und indische Küchen 🍜       |    [Klassifizierung](4-Classification/README.md)        | Erstellen Sie eine Empfehlungs-Web-App basierend auf Ihrem Modell                                                                |                                              [Python](4-Classification/4-Applied/README.md)                                              |                         Jen                        |
+|      14        |                   Einführung in Clustering                     |        [Clustering](5-Clustering/README.md)              | Daten bereinigen, vorbereiten und visualisieren; Einführung in Clustering                                                       |         [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html)         |      Jen • Eric Wanjau       |
+|      15        |              Erforschung nigerianischer Musikgeschmäcker 🎧   |        [Clustering](5-Clustering/README.md)              | Erforschen Sie die K-Means Clustering-Methode                                                                                   |           [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html)           |      Jen • Eric Wanjau       |
+|      16        |        Einführung in die Verarbeitung natürlicher Sprache ☕️  |   [Verarbeitung natürlicher Sprache](6-NLP/README.md)    | Lernen Sie die Grundlagen der NLP, indem Sie einen einfachen Bot erstellen                                                     |                                             [Python](6-NLP/1-Introduction-to-NLP/README.md)                                              |                       Stephen                      |
+|      17        |                      Häufige NLP Aufgaben ☕️                  |   [Verarbeitung natürlicher Sprache](6-NLP/README.md)    | Vertiefen Sie Ihr NLP-Wissen durch das Verständnis gängiger Aufgaben bei der Arbeit mit Sprachstrukturen                        |                                                    [Python](6-NLP/2-Tasks/README.md)                                                     |                       Stephen                      |
+|      18        |             Übersetzung und Stimmungsanalyse ♥️               |   [Verarbeitung natürlicher Sprache](6-NLP/README.md)    | Übersetzung und Stimmungsanalyse mit Jane Austen                                                                              |                                            [Python](6-NLP/3-Translation-Sentiment/README.md)                                             |                       Stephen                      |
+|      19        |                  Romantische Hotels in Europa ♥️               |   [Verarbeitung natürlicher Sprache](6-NLP/README.md)    | Stimmungsanalyse mit Hotelbewertungen 1                                                                                        |                                               [Python](6-NLP/4-Hotel-Reviews-1/README.md)                                                |                       Stephen                      |
+|      20        |                  Romantische Hotels in Europa ♥️               |   [Verarbeitung natürlicher Sprache](6-NLP/README.md)    | Stimmungsanalyse mit Hotelbewertungen 2                                                                                        |                                               [Python](6-NLP/5-Hotel-Reviews-2/README.md)                                                |                       Stephen                      |
+|      21        |            Einführung in die Zeitreihenprognose               |        [Zeitreihen](7-TimeSeries/README.md)               | Einführung in die Zeitreihenprognose                                                                                          |                                             [Python](7-TimeSeries/1-Introduction/README.md)                                              |                      Francesca                     |
+|      22        | ⚡️ Weltweiter Energieverbrauch ⚡️ - Zeitreihenprognose mit ARIMA |        [Zeitreihen](7-TimeSeries/README.md)               | Zeitreihenprognose mit ARIMA                                                                                                  |                                                 [Python](7-TimeSeries/2-ARIMA/README.md)                                                 |                      Francesca                     |
+|      23        |  ⚡️ Weltweiter Energieverbrauch ⚡️ - Zeitreihenprognose mit SVR  |        [Zeitreihen](7-TimeSeries/README.md)               | Zeitreihenprognose mit Support Vector Regressor                                                                                |                                                  [Python](7-TimeSeries/3-SVR/README.md)                                                  |                       Anirban                      |
+|      24        |             Einführung ins Reinforcement Learning             | [Reinforcement Learning](8-Reinforcement/README.md)        | Einführung in Reinforcement Learning mit Q-Learning                                                                           |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                      |
+|      25        |                 Hilf Peter, den Wolf zu vermeiden! 🐺         | [Reinforcement Learning](8-Reinforcement/README.md)        | Reinforcement Learning Gym                                                                                                    |                                                [Python](8-Reinforcement/2-Gym/README.md)                                                 |                        Dmitry                      |
+|  Nachtrag     |            Anwendungsbeispiele und Szenarien aus der Praxis    |      [ML in the Wild](9-Real-World/README.md)             | Interessante und aufschlussreiche reale Anwendungen klassischer ML                                                              |                                             [Lektion](9-Real-World/1-Applications/README.md)                                              |                         Team                       |
+|  Nachtrag     |            Modellsuche und -debugging in ML mit RAI Dashboard |      [ML in the Wild](9-Real-World/README.md)             | Modellsuche und Debugging im maschinellen Lernen mithilfe der Responsible AI Dashboard-Komponenten                              |                                             [Lektion](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                 |
+
+> [Finden Sie alle zusätzlichen Ressourcen zu diesem Kurs in unserer Microsoft Learn Sammlung](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
 ## Offline-Zugriff
 
-Sie können diese Dokumentation offline mit [Docsify](https://docsify.js.org/#/) ausführen. Erstellen Sie einen Fork dieses Repositories, [Docsify installieren](https://docsify.js.org/#/quickstart) auf Ihrer lokalen Maschine, und dann im Stammordner dieses Repositories geben Sie `docsify serve` ein. Die Website wird auf Port 3000 auf Ihrem lokalen Host bereitgestellt: `localhost:3000`.
+Sie können diese Dokumentation offline mit [Docsify](https://docsify.js.org/#/) nutzen. Forken Sie dieses Repository, [installieren Sie Docsify](https://docsify.js.org/#/quickstart) auf Ihrem lokalen Rechner und geben Sie dann im Stammverzeichnis dieses Repositories `docsify serve` ein. Die Website wird auf Port 3000 an Ihrem lokalen Host bereitgestellt: `localhost:3000`.
 
 ## PDFs
 
-Ein PDF des Lehrplans mit Links finden Sie [hier](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+Ein PDF des Curriculums mit Links finden Sie [hier](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
 
-## 🎒 Weitere Kurse 
+## 🎒 Weitere Kurse
 
-Unser Team bietet weitere Kurse an! Schauen Sie sich diese an:
+Unser Team produziert weitere Kurse! Schauen Sie rein:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
-[![LangChain4j für Einsteiger](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 für Einsteiger](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 für Anfänger](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 für Anfänger](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 für Einsteiger](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 für Einsteiger](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 für Einsteiger](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)
-[![KI-Agenten für Einsteiger](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 für Anfänger](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 für Anfänger](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 für Anfänger](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 für Anfänger](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### Generative AI-Reihe
-[![Generative AI für Einsteiger](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+### Generative AI Series
+[![Generative KI für Anfänger](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 KI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
 [![Generative KI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
 [![Generative KI (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)
 
 ---
  
-### Kerninhalte
+### Kernlernen
 [![ML für Anfänger](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![Data Science für Anfänger](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)
+[![Datenwissenschaft für Anfänger](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)
 [![KI für Anfänger](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)
 [![Cybersicherheit für Anfänger](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)
 [![Webentwicklung für Anfänger](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)
@@ -199,25 +208,25 @@ Unser Team bietet weitere Kurse an! Schauen Sie sich diese an:
 
 ---
  
-### Copilot-Serie
+### Copilot-Reihe
 [![Copilot für KI-Paarprogrammierung](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
 [![Copilot für C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Copilot-Abenteuer](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot Abenteuer](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 -->
 
-## Hilfe
+## Hilfe erhalten
 
-Wenn Sie feststecken oder Fragen zum Erstellen von KI-Apps haben, schließen Sie sich anderen Lernenden und erfahrenen Entwicklern an und diskutieren Sie über MCP. Es ist eine unterstützende Community, in der Fragen willkommen sind und Wissen frei geteilt wird.
+Wenn Sie nicht weiterkommen oder Fragen zum Erstellen von KI-Anwendungen haben, schließen Sie sich anderen Lernenden und erfahrenen Entwicklern in Diskussionen über MCP an. Es ist eine unterstützende Gemeinschaft, in der Fragen willkommen sind und Wissen frei geteilt wird.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-Wenn Sie Produktfeedback haben oder beim Erstellen auf Fehler stoßen, besuchen Sie:
+Wenn Sie Produktfeedback oder Fehler beim Entwickeln haben, besuchen Sie:
 
-[![Microsoft Foundry Entwicklerforum](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 -->
-Haftungsausschluss:
-Dieses Dokument wurde mit dem KI‑Übersetzungsdienst Co‑op Translator (https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, können automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten. Das Originaldokument in seiner Ausgangssprache ist als maßgebliche Quelle zu betrachten. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Verwendung dieser Übersetzung entstehen.
+**Haftungsausschluss**:
+Dieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, möchten wir darauf hinweisen, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Originalsprache ist als maßgebliche Quelle zu betrachten. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Nutzung dieser Übersetzung entstehen.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/el/README.md b/translations/el/README.md
index 81c5fbbd4..d891558a4 100644
--- a/translations/el/README.md
+++ b/translations/el/README.md
@@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Διοργανώνουμε μια σειρά Discord "Μάθε με AI" σε εξέλιξη — μάθετε περισσότερα και εγγραφείτε στη σειρά στο [Learn with AI Series](https://aka.ms/learnwithai/discord) από 18 - 30 Σεπτεμβρίου 2025. Θα λάβετε συμβουλές και κόλπα για τη χρήση του GitHub Copilot για Data Science.
 
-![Σειρά Learn with AI](../../translated_images/el/3.9b58fd8d6c373c20.png)
+![Σειρά Learn with AI](../../translated_images/el/3.9b58fd8d6c373c20.webp)
 
 # Μηχανική Μάθηση για Αρχάριους - Ένα Πρόγραμμα Σπουδών
 
@@ -80,7 +80,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Μερικά από τα μαθήματα είναι διαθέσιμα ως βίντεο σύντομης μορφής. Μπορείτε να βρείτε όλα αυτά ενσωματωμένα στα μαθήματα, ή στη [playlist ML for Beginners στο κανάλι Microsoft Developer στο YouTube](https://aka.ms/ml-beginners-videos) κάνοντας κλικ στην εικόνα παρακάτω.
 
-[![Μπάνερ ML για αρχάριους](../../translated_images/el/ml-for-beginners-video-banner.63f694a100034bc6.png)](https://aka.ms/ml-beginners-videos)
+[![Μπάνερ ML για αρχάριους](../../translated_images/el/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos)
 
 ---
 
diff --git a/translations/et/1-Introduction/1-intro-to-ML/README.md b/translations/et/1-Introduction/1-intro-to-ML/README.md
index 8980d385a..b50d4d7af 100644
--- a/translations/et/1-Introduction/1-intro-to-ML/README.md
+++ b/translations/et/1-Introduction/1-intro-to-ML/README.md
@@ -42,7 +42,7 @@ Termin 'masinõpe' on üks tänapäeva populaarsemaid ja sagedamini kasutatavaid
 ---
 ## Hüppekurv
 
-![ml hüppekurv](../../../../translated_images/et/hype.07183d711a17aafe.png)
+![ml hüppekurv](../../../../translated_images/et/hype.07183d711a17aafe.webp)
 
 > Google Trends näitab termini 'masinõpe' hiljutist hüppekurvi
 
@@ -69,7 +69,7 @@ Kuigi termineid võib segamini ajada, on masinõpe (ML) tehisintellekti oluline
 ---
 ## AI, ML, süvaõpe
 
-![AI, ML, süvaõpe, andmeteadus](../../../../translated_images/et/ai-ml-ds.537ea441b124ebf6.png)
+![AI, ML, süvaõpe, andmeteadus](../../../../translated_images/et/ai-ml-ds.537ea441b124ebf6.webp)
 
 > Diagramm, mis näitab AI, ML-i, süvaõppe ja andmeteaduse vahelisi seoseid. Infograafik autorilt [Jen Looper](https://twitter.com/jenlooper), inspireeritud [sellest graafikust](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
diff --git a/translations/et/1-Introduction/2-history-of-ML/README.md b/translations/et/1-Introduction/2-history-of-ML/README.md
index 4d48cd51e..c19423d2a 100644
--- a/translations/et/1-Introduction/2-history-of-ML/README.md
+++ b/translations/et/1-Introduction/2-history-of-ML/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Masinõppe ajalugu
 
-![Masinõppe ajaloo kokkuvõte visandmärkmetes](../../../../translated_images/et/ml-history.a1bdfd4ce1f464d9.png)
+![Masinõppe ajaloo kokkuvõte visandmärkmetes](../../../../translated_images/et/ml-history.a1bdfd4ce1f464d9.webp)
 > Visandmärkmed: [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)
@@ -71,14 +71,14 @@ Uuringuid rahastasid hästi valitsusasutused, arvutusvõimekus ja algoritmid are
 
 * [Shakey robot](https://wikipedia.org/wiki/Shakey_the_robot), kes suutis liikuda ja otsustada, kuidas ülesandeid 'intelligentselt' täita.
 
-    ![Shakey, intelligentne robot](../../../../translated_images/et/shakey.4dc17819c447c05b.jpg)
+    ![Shakey, intelligentne robot](../../../../translated_images/et/shakey.4dc17819c447c05b.webp)
     > Shakey aastal 1972
 
 ---
 
 * Eliza, varajane 'vestlusrobot', suutis inimestega vestelda ja toimida primitiivse 'terapeudina'. Õpite Eliza kohta rohkem NLP õppetundides.
 
-    ![Eliza, robot](../../../../translated_images/et/eliza.84397454cda9559b.png)
+    ![Eliza, robot](../../../../translated_images/et/eliza.84397454cda9559b.webp)
     > Eliza versioon, vestlusrobot
 
 ---
diff --git a/translations/et/1-Introduction/3-fairness/README.md b/translations/et/1-Introduction/3-fairness/README.md
index 86949bf1d..85c007095 100644
--- a/translations/et/1-Introduction/3-fairness/README.md
+++ b/translations/et/1-Introduction/3-fairness/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Masinõppe lahenduste loomine vastutustundliku tehisintellektiga
 
-![Vastutustundliku tehisintellekti kokkuvõte masinõppes sketšimärkmetes](../../../../translated_images/et/ml-fairness.ef296ebec6afc98a.png)
+![Vastutustundliku tehisintellekti kokkuvõte masinõppes sketšimärkmetes](../../../../translated_images/et/ml-fairness.ef296ebec6afc98a.webp)
 > Sketšimärkmed: [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Loengu-eelne viktoriin](https://ff-quizzes.netlify.app/en/ml/)
@@ -51,10 +51,10 @@ Tehisintellekti süsteemid peaksid kohtlema kõiki õiglaselt ja vältima sarnas
 - **Üle- või alaesindatus**. Idee, et teatud rühma ei nähta teatud ametis, ja iga teenus või funktsioon, mis seda jätkuvalt edendab, aitab kaasa kahjule.
 - **Stereotüpiseerimine**. Teatud rühma seostamine eelnevalt määratud omadustega. Näiteks inglise ja türgi keele vahel tõlkiv süsteem võib eksida sõnadega, millel on sooga seotud stereotüüpsed seosed.
 
-![Tõlge türgi keelde](../../../../translated_images/et/gender-bias-translate-en-tr.f185fd8822c2d437.png)
+![Tõlge türgi keelde](../../../../translated_images/et/gender-bias-translate-en-tr.f185fd8822c2d437.webp)
 > Tõlge türgi keelde
 
-![Tõlge tagasi inglise keelde](../../../../translated_images/et/gender-bias-translate-tr-en.4eee7e3cecb8c70e.png)
+![Tõlge tagasi inglise keelde](../../../../translated_images/et/gender-bias-translate-tr-en.4eee7e3cecb8c70e.webp)
 > Tõlge tagasi inglise keelde
 
 Tehisintellekti süsteemide kavandamisel ja testimisel peame tagama, et tehisintellekt oleks õiglane ega oleks programmeeritud tegema eelarvamuslikke või diskrimineerivaid otsuseid, mida ka inimestel on keelatud teha. Õigluse tagamine tehisintellektis ja masinõppes jääb keeruliseks sotsiaal-tehniliseks väljakutseks.
@@ -97,7 +97,7 @@ Tehisintellekti süsteemid peaksid olema arusaadavad. Läbipaistvuse oluline osa
 
 Inimesed, kes kavandavad ja rakendavad tehisintellekti süsteeme, peavad vastutama selle eest, kuidas nende süsteemid toimivad. Vastutuse vajadus on eriti oluline tundlike tehnoloogiate, nagu näotuvastus, puhul. Viimasel ajal on näotuvastustehnoloogia järele olnud kasvav nõudlus, eriti õiguskaitseorganisatsioonide poolt, kes näevad tehnoloogia potentsiaali näiteks kadunud laste leidmisel. Kuid need tehnoloogiad võivad valitsuse poolt potentsiaalselt ohustada kodanike põhiõigusi, võimaldades näiteks konkreetsete isikute pidevat jälgimist. Seetõttu peavad andmeteadlased ja organisatsioonid vastutama selle eest, kuidas nende tehisintellekti süsteem mõjutab üksikisikuid või ühiskonda.
 
-[![Juhtiv tehisintellekti teadlane hoiatab massilise jälgimise eest näotuvastuse kaudu](../../../../translated_images/et/accountability.41d8c0f4b85b6231.png)](https://www.youtube.com/watch?v=Wldt8P5V6D0 "Microsofti lähenemine vastutustundlikule tehisintellektile")
+[![Juhtiv tehisintellekti teadlane hoiatab massilise jälgimise eest näotuvastuse kaudu](../../../../translated_images/et/accountability.41d8c0f4b85b6231.webp)](https://www.youtube.com/watch?v=Wldt8P5V6D0 "Microsofti lähenemine vastutustundlikule tehisintellektile")
 
 > 🎥 Klõpsake ülaloleval pildil, et vaadata videot: Hoiatused massilise jälgimise eest näotuvastuse kaudu
 
diff --git a/translations/et/1-Introduction/4-techniques-of-ML/README.md b/translations/et/1-Introduction/4-techniques-of-ML/README.md
index 72c79c521..38ecfdb48 100644
--- a/translations/et/1-Introduction/4-techniques-of-ML/README.md
+++ b/translations/et/1-Introduction/4-techniques-of-ML/README.md
@@ -97,7 +97,7 @@ Masinõppe kontekstis viitab mudeli sobitamine mudeli aluseks oleva funktsiooni
 
 🎓 **Alasobitamine** ja **ülesobitamine** on levinud probleemid, mis halvendavad mudeli kvaliteeti, kuna mudel sobitub kas liiga halvasti või liiga hästi. See põhjustab mudeli ennustusi, mis on kas liiga tihedalt seotud või liiga lõdvalt seotud treeningandmetega. Ülesobitunud mudel ennustab treeningandmeid liiga hästi, kuna see on õppinud andmete üksikasju ja müra liiga hästi. Alasobitunud mudel pole täpne, kuna see ei suuda täpselt analüüsida ei oma treeningandmeid ega andmeid, mida see pole veel "näinud".
 
-![ülesobitunud mudel](../../../../translated_images/et/overfitting.1c132d92bfd93cb6.png)
+![ülesobitunud mudel](../../../../translated_images/et/overfitting.1c132d92bfd93cb6.webp)
 > Infograafik: [Jen Looper](https://twitter.com/jenlooper)
 
 ## Parameetrite häälestamine
diff --git a/translations/et/1-Introduction/README.md b/translations/et/1-Introduction/README.md
index fa0134124..f77b5111f 100644
--- a/translations/et/1-Introduction/README.md
+++ b/translations/et/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Selles õppekava osas tutvustatakse teile masinõppe valdkonna aluskontseptsioone, mis see on, ning saate teada selle ajaloost ja tehnikatest, mida teadlased selle valdkonnaga töötamiseks kasutavad. Uurime koos seda uut ML-i maailma!
 
-![globe](../../../translated_images/et/globe.59f26379ceb40428.jpg)
+![globe](../../../translated_images/et/globe.59f26379ceb40428.webp)
 > Foto autorilt <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> lehel <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Õppetunnid
diff --git a/translations/et/2-Regression/1-Tools/README.md b/translations/et/2-Regression/1-Tools/README.md
index 7ff962a91..533da887a 100644
--- a/translations/et/2-Regression/1-Tools/README.md
+++ b/translations/et/2-Regression/1-Tools/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Alusta Pythoni ja Scikit-learniga regressioonimudelite jaoks
 
-![Regressioonide kokkuvõte visuaalses märkmes](../../../../translated_images/et/ml-regression.4e4f70e3b3ed446e.png)
+![Regressioonide kokkuvõte visuaalses märkmes](../../../../translated_images/et/ml-regression.4e4f70e3b3ed446e.webp)
 
 > Visuaalne märge: [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
@@ -81,7 +81,7 @@ Selles kaustas leiad faili _notebook.ipynb_.
     hello notebook
     ```
 
-![VS Code avatud märkmikuga](../../../../translated_images/et/notebook.4a3ee31f396b8832.jpg)
+![VS Code avatud märkmikuga](../../../../translated_images/et/notebook.4a3ee31f396b8832.webp)
 
 Sa saad oma koodi vaheldumisi kommentaaridega täiendada, et märkmikku ise dokumenteerida.
 
@@ -211,7 +211,7 @@ Uues koodilahtris laadi diabeedi andmekogum, kutsudes `load_diabetes()`. Sisend
     plt.show()
     ```
 
-   ![hajusdiagramm, mis näitab diabeediga seotud andmepunkte](../../../../translated_images/et/scatterplot.ad8b356bcbb33be6.png)
+   ![hajusdiagramm, mis näitab diabeediga seotud andmepunkte](../../../../translated_images/et/scatterplot.ad8b356bcbb33be6.webp)
 ✅ Mõtle veidi, mis siin toimub. Sirgjoon kulgeb läbi paljude väikeste andmepunktide, kuid mida see täpselt teeb? Kas näed, kuidas saaksid seda joont kasutada, et ennustada, kuhu uus, seni nägemata andmepunkt peaks graafiku y-telje suhtes sobituma? Proovi sõnastada selle mudeli praktiline kasutus.
 
 Palju õnne, sa ehitasid oma esimese lineaarse regressioonimudeli, tegid sellega ennustuse ja kuvad selle graafikul!
diff --git a/translations/et/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/et/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
index 7b8c07a05..7ceffc5a3 100644
--- a/translations/et/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
+++ b/translations/et/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -46,7 +46,7 @@
     "Sellega öeldes, alustame selle ülesandega!\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/encouRage.e75d5fe0367fb913.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/encouRage.e75d5fe0367fb913.webp\"\n",
     "   width=\"630\"/>\n",
     "   <figcaption>Kunstiteos: @allison_horst</figcaption>\n"
    ],
diff --git a/translations/et/2-Regression/2-Data/README.md b/translations/et/2-Regression/2-Data/README.md
index b737d85d6..177b119cf 100644
--- a/translations/et/2-Regression/2-Data/README.md
+++ b/translations/et/2-Regression/2-Data/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Ehita regressioonimudel Scikit-learniga: andmete ettevalmistamine ja visualiseerimine
 
-![Andmete visualiseerimise infograafika](../../../../translated_images/et/data-visualization.54e56dded7c1a804.png)
+![Andmete visualiseerimise infograafika](../../../../translated_images/et/data-visualization.54e56dded7c1a804.webp)
 
 Infograafika autor: [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
@@ -183,7 +183,7 @@ Proovi luua mõned põhilised graafikud, et kuvada just loodud andmeraami. Mida
     plt.show()
     ```
 
-    ![Hajuvusdiagramm, mis näitab hinna ja kuu suhet](../../../../translated_images/et/scatterplot.b6868f44cbd2051c.png)
+    ![Hajuvusdiagramm, mis näitab hinna ja kuu suhet](../../../../translated_images/et/scatterplot.b6868f44cbd2051c.webp)
 
     Kas see on kasulik graafik? Kas miski selles üllatab sind?
 
@@ -200,7 +200,7 @@ Et graafikud kuvaksid kasulikke andmeid, tuleb andmeid tavaliselt kuidagi rühmi
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![Tulpdiagramm, mis näitab hinna ja kuu suhet](../../../../translated_images/et/barchart.a833ea9194346d76.png)
+    ![Tulpdiagramm, mis näitab hinna ja kuu suhet](../../../../translated_images/et/barchart.a833ea9194346d76.webp)
 
     See on kasulikum andmete visualiseerimine! Näib, et kõrvitsate kõrgeim hind esineb septembris ja oktoobris. Kas see vastab sinu ootustele? Miks või miks mitte?
 
diff --git a/translations/et/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/et/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
index 5558a0488..2438b303a 100644
--- a/translations/et/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
+++ b/translations/et/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -45,12 +45,12 @@
     "\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/unruly_data.0eedc7ced92d2d91.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/unruly_data.0eedc7ced92d2d91.webp\"\n",
     "   width=\"700\"/>\n",
     "   <figcaption>Kunstiteos: @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Kunstiteos: \\@allison_horst](../../../../../../translated_images/et/unruly_data.0eedc7ced92d2d91.jpg)<br>Kunstiteos: \\@allison_horst-->\n"
+    "<!--![Kunstiteos: \\@allison_horst](../../../../../../translated_images/et/unruly_data.0eedc7ced92d2d91.webp)<br>Kunstiteos: \\@allison_horst-->\n"
    ],
    "metadata": {
     "id": "Pg5aexcOPqAZ"
@@ -226,7 +226,7 @@
     "## 3. Dplyr: Andmete manipuleerimise grammatika\n",
     "\n",
     "<p>\n",
-    "   <img src=\"../../../../../../translated_images/et/dplyr_wrangling.f5f99c64fd4580f1.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/dplyr_wrangling.f5f99c64fd4580f1.webp\"\n",
     "   width=\"569\"/>\n",
     "   <figcaption>Kunstiteos: @allison_horst</figcaption>\n"
    ],
@@ -529,12 +529,12 @@
     "## 4. Andmete visualiseerimine ggplot2 abil\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/data-visualization.54e56dded7c1a804.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/data-visualization.54e56dded7c1a804.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Infograafika autor: Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infograafika autor: Dasani Madipalli](../../../../../../translated_images/et/data-visualization.54e56dded7c1a804.png){width=\"600\"}-->\n",
+    "<!--![Infograafika autor: Dasani Madipalli](../../../../../../translated_images/et/data-visualization.54e56dded7c1a804.webp){width=\"600\"}-->\n",
     "\n",
     "On olemas *tark* ütlus, mis kõlab järgmiselt:\n",
     "\n",
diff --git a/translations/et/2-Regression/3-Linear/README.md b/translations/et/2-Regression/3-Linear/README.md
index 808455893..b4d41c24a 100644
--- a/translations/et/2-Regression/3-Linear/README.md
+++ b/translations/et/2-Regression/3-Linear/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Ehita regressioonimudel Scikit-learniga: neli viisi regressiooniks
 
-![Lineaarse ja polünoomse regressiooni infograafika](../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.png)
+![Lineaarse ja polünoomse regressiooni infograafika](../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp)
 > Infograafika autor: [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)
 
@@ -66,13 +66,13 @@ Teeme seda, kuna soovime modelleerida joont, millel on kõigi meie andmepunktide
 >
 > `X` on 'selgitav muutuja'. `Y` on 'sõltuv muutuja'. Joone kalle on `b` ja `a` on y-teljelõige, mis viitab `Y` väärtusele, kui `X = 0`.
 >
->![kalle arvutamine](../../../../translated_images/et/slope.f3c9d5910ddbfcf9.png)
+>![kalle arvutamine](../../../../translated_images/et/slope.f3c9d5910ddbfcf9.webp)
 >
 > Kõigepealt arvutage kalle `b`. Infograafika autor: [Jen Looper](https://twitter.com/jenlooper)
 >
 > Teisisõnu, viidates meie kõrvitsate andmete algsele küsimusele: "ennusta kõrvitsa hinda busheli kohta kuu järgi", viitaks `X` hinnale ja `Y` müügikuule.
 >
->![võrrandi täitmine](../../../../translated_images/et/calculation.a209813050a1ddb1.png)
+>![võrrandi täitmine](../../../../translated_images/et/calculation.a209813050a1ddb1.webp)
 >
 > Arvutage Y väärtus. Kui maksate umbes 4 dollarit, peab olema aprill! Infograafika autor: [Jen Looper](https://twitter.com/jenlooper)
 >
@@ -114,11 +114,11 @@ Nüüd, kui olete aru saanud lineaarse regressiooni matemaatikast, loome regress
 
 Eelmises õppetunnis olete tõenäoliselt näinud, et keskmine hind erinevate kuude kohta näeb välja selline:
 
-<img alt="Keskmine hind kuu järgi" src="../../../../translated_images/et/barchart.a833ea9194346d76.png" width="50%"/>
+<img alt="Keskmine hind kuu järgi" src="../../../../translated_images/et/barchart.a833ea9194346d76.webp" width="50%"/>
 
 See viitab sellele, et peaks olema mingi korrelatsioon, ja me võime proovida treenida lineaarse regressiooni mudelit, et ennustada seost `Kuu` ja `Hinna` vahel või `AastaPäeva` ja `Hinna` vahel. Siin on hajuvusdiagramm, mis näitab viimast seost:
 
-<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/scatter-dayofyear.bc171c189c9fd553.png" width="50%" /> 
+<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/scatter-dayofyear.bc171c189c9fd553.webp" width="50%" /> 
 
 Vaatame, kas korrelatsioon on olemas, kasutades funktsiooni `corr`:
 
@@ -137,7 +137,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()):
     ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)
 ```
 
-<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/scatter-dayofyear-color.65790faefbb9d54f.png" width="50%" /> 
+<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/scatter-dayofyear-color.65790faefbb9d54f.webp" width="50%" /> 
 
 Meie uurimine viitab sellele, et sordil on müügikuupäevast suurem mõju üldisele hinnale. Seda näeme ka tulpdiagrammist:
 
@@ -145,7 +145,7 @@ Meie uurimine viitab sellele, et sordil on müügikuupäevast suurem mõju üldi
 new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')
 ```
 
-<img alt="Tulpdiagramm hinna ja sordi vahel" src="../../../../translated_images/et/price-by-variety.744a2f9925d9bcb4.png" width="50%" /> 
+<img alt="Tulpdiagramm hinna ja sordi vahel" src="../../../../translated_images/et/price-by-variety.744a2f9925d9bcb4.webp" width="50%" /> 
 
 Keskendume hetkeks ainult ühele kõrvitsasordile, 'pie type', ja vaatame, millist mõju kuupäev hinnale avaldab:
 
@@ -153,7 +153,7 @@ Keskendume hetkeks ainult ühele kõrvitsasordile, 'pie type', ja vaatame, milli
 pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']
 pie_pumpkins.plot.scatter('DayOfYear','Price') 
 ```
-<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/pie-pumpkins-scatter.d14f9804a53f927e.png" width="50%" /> 
+<img alt="Hajuvusdiagramm hinna ja aasta päeva vahel" src="../../../../translated_images/et/pie-pumpkins-scatter.d14f9804a53f927e.webp" width="50%" /> 
 
 Kui arvutame nüüd korrelatsiooni `Hinna` ja `AastaPäeva` vahel, kasutades funktsiooni `corr`, saame tulemuseks umbes `-0.27` - mis tähendab, et ennustava mudeli treenimine on mõistlik.
 
@@ -228,7 +228,7 @@ plt.scatter(X_test,y_test)
 plt.plot(X_test,pred)
 ```
 
-<img alt="Lineaarne regressioon" src="../../../../translated_images/et/linear-results.f7c3552c85b0ed1c.png" width="50%" />
+<img alt="Lineaarne regressioon" src="../../../../translated_images/et/linear-results.f7c3552c85b0ed1c.webp" width="50%" />
 
 ## Polünoomiline regressioon
 
@@ -257,7 +257,7 @@ pipeline.fit(X_train,y_train)
 
 Pipeline'i saab kasutada samamoodi nagu algset `LinearRegression` objekti, st saame pipeline'i `fit`-ida ja seejärel kasutada `predict`, et saada prognoositulemused. Siin on graafik, mis näitab testandmeid ja lähenduskõverat:
 
-<img alt="Polünoomiline regressioon" src="../../../../translated_images/et/poly-results.ee587348f0f1f60b.png" width="50%" />
+<img alt="Polünoomiline regressioon" src="../../../../translated_images/et/poly-results.ee587348f0f1f60b.webp" width="50%" />
 
 Polünoomilist regressiooni kasutades saame veidi madalama MSE ja kõrgema determinatsiooni, kuid mitte märkimisväärselt. Peame arvesse võtma ka teisi omadusi!
 
@@ -275,7 +275,7 @@ Ideaalis tahame olla võimelised prognoosima hindu erinevate kõrvitsasortide ja
 
 Siin näed, kuidas keskmine hind sõltub sordist:
 
-<img alt="Keskmine hind sordi järgi" src="../../../../translated_images/et/price-by-variety.744a2f9925d9bcb4.png" width="50%" />
+<img alt="Keskmine hind sordi järgi" src="../../../../translated_images/et/price-by-variety.744a2f9925d9bcb4.webp" width="50%" />
 
 Sordi arvesse võtmiseks peame esmalt selle numbriliseks vormiks teisendama ehk **kodeerima**. Selleks on mitu võimalust:
 
diff --git a/translations/et/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/et/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
index 4c8581d60..e761dd83d 100644
--- a/translations/et/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
+++ b/translations/et/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -37,7 +37,7 @@
    "source": [
     "## Lineaarne ja polünoomne regressioon kõrvitsate hinnastamiseks - Õppetund 3\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infograafika autor: Dasani Madipalli</figcaption>\n",
     "\n",
@@ -159,12 +159,12 @@
     "## **2. Tants andmetega: andmeraami loomine, mida kasutatakse modelleerimiseks**\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/janitor.e4a77dd3d3e6a32e.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/janitor.e4a77dd3d3e6a32e.webp\"\n",
     "   width=\"700\"/>\n",
     "   <figcaption>Kunstiteos: @allison_horst</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Kunstiteos: \\@allison_horst](../../../../../../translated_images/et/janitor.e4a77dd3d3e6a32e.jpg){width=\"700\"}-->\n"
+    "<!--![Kunstiteos: \\@allison_horst](../../../../../../translated_images/et/janitor.e4a77dd3d3e6a32e.webp){width=\"700\"}-->\n"
    ],
    "metadata": {
     "id": "WdUKXk7Bs8-V"
@@ -453,7 +453,7 @@
     "Selleks vajad kahte lisakäsku: `prep()` ja `bake()`, ning nagu alati, aitavad meie väikesed R-sõbrad [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) sul sellest paremini aru saada!\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/recipes.9ad10d8a4056bf89.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/recipes.9ad10d8a4056bf89.webp\"\n",
     "   width=\"550\"/>\n",
     "   <figcaption>Illustratsioon autorilt @allison_horst</figcaption>\n"
    ],
@@ -564,12 +564,12 @@
     "## 4. Lineaarse regressioonimudeli loomine\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infograafik: Dasani Madipalli</figcaption>\n",
     "\n",
     "\n",
-    "<!--![Infograafik: Dasani Madipalli](../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.png){width=\"800\"}-->\n"
+    "<!--![Infograafik: Dasani Madipalli](../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp){width=\"800\"}-->\n"
    ],
    "metadata": {
     "id": "YqXjLuWavNxW"
@@ -805,7 +805,7 @@
     "## 5. Loo polünoomse regressiooni mudel\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/linear-polynomial.5523c7cb6576ccab.webp\"\n",
     "   width=\"800\"/>\n",
     "   <figcaption>Infograafik: Dasani Madipalli</figcaption>\n"
    ],
diff --git a/translations/et/2-Regression/4-Logistic/README.md b/translations/et/2-Regression/4-Logistic/README.md
index 7776eb99f..81072c972 100644
--- a/translations/et/2-Regression/4-Logistic/README.md
+++ b/translations/et/2-Regression/4-Logistic/README.md
@@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA:
 -->
 # Logistiline regressioon kategooriate ennustamiseks
 
-![Logistilise ja lineaarse regressiooni infograafik](../../../../translated_images/et/linear-vs-logistic.ba180bf95e7ee667.png)
+![Logistilise ja lineaarse regressiooni infograafik](../../../../translated_images/et/linear-vs-logistic.ba180bf95e7ee667.webp)
 
 ## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ml/)
 
@@ -52,7 +52,7 @@ Logistiline regressioon erineb lineaarse regressioonist, mida sa õppisid varem,
 
 Logistiline regressioon ei paku samu funktsioone kui lineaarne regressioon. Esimene pakub ennustust binaarse kategooria kohta ("valge või mitte valge"), samas kui teine suudab ennustada pidevaid väärtusi, näiteks arvestades kõrvitsa päritolu ja koristusaega, _kui palju selle hind tõuseb_.
 
-![Kõrvitsa klassifikatsioonimudel](../../../../translated_images/et/pumpkin-classifier.562771f104ad5436.png)
+![Kõrvitsa klassifikatsioonimudel](../../../../translated_images/et/pumpkin-classifier.562771f104ad5436.webp)
 > Infograafik autorilt [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### Muud klassifikatsioonid
@@ -62,7 +62,7 @@ On olemas ka teisi logistilise regressiooni tüüpe, sealhulgas multinomiaalne j
 - **Multinomiaalne**, mis hõlmab rohkem kui ühte kategooriat - "Oranž, Valge ja Triibuline".
 - **Ordinaalne**, mis hõlmab järjestatud kategooriaid, kasulik, kui tahame oma tulemusi loogiliselt järjestada, näiteks kõrvitsad, mis on järjestatud piiratud arvu suuruste järgi (mini, väike, keskmine, suur, XL, XXL).
 
-![Multinomiaalne vs ordinaalne regressioon](../../../../translated_images/et/multinomial-vs-ordinal.36701b4850e37d86.png)
+![Multinomiaalne vs ordinaalne regressioon](../../../../translated_images/et/multinomial-vs-ordinal.36701b4850e37d86.webp)
 
 ### Muutujad EI PEA korreleeruma
 
@@ -118,7 +118,7 @@ Seaborn pakub huvitavaid viise andmete visualiseerimiseks. Näiteks saad võrrel
     )
     ```
 
-    ![Visualiseeritud andmete ruudustik](../../../../translated_images/et/pumpkins_catplot_1.c55c409b71fea2ec.png)
+    ![Visualiseeritud andmete ruudustik](../../../../translated_images/et/pumpkins_catplot_1.c55c409b71fea2ec.webp)
 
     Vaadates andmeid, näed, kuidas `Color` andmed seostuvad `Variety`-ga.
 
@@ -197,7 +197,7 @@ Parim viis sellise analüüsi tegemiseks on andmete graafikule kandmine. Kasutam
     g.set(xlabel="Item Size", ylabel="").set(xlim=(0,6))
     g.set_titles(row_template="{row_name}")
 ```
-![Visualiseeritud andmete kategooriline graafik](../../../../translated_images/et/pumpkins_catplot_2.87a354447880b388.png)
+![Visualiseeritud andmete kategooriline graafik](../../../../translated_images/et/pumpkins_catplot_2.87a354447880b388.webp)
 
 ### Kasuta 'swarm' graafikut
 
@@ -215,7 +215,7 @@ Saad visualiseerida muutujaid kõrvuti Seaborn graafikutega.
     sns.swarmplot(x="Color", y="ord__Item Size", data=encoded_pumpkins, palette=palette)
     ```
 
-    ![Visualiseeritud andmete 'swarm'](../../../../translated_images/et/swarm_2.efeacfca536c2b57.png)
+    ![Visualiseeritud andmete 'swarm'](../../../../translated_images/et/swarm_2.efeacfca536c2b57.webp)
 
 **Ole ettevaatlik**: ülaltoodud kood võib genereerida hoiatuse, kuna Seaborn ei suuda esitada sellist hulka andmepunkte 'swarm' graafikus. Võimalik lahendus on vähendada markeri suurust, kasutades 'size' parameetrit. Kuid ole teadlik, et see mõjutab graafiku loetavust.
 
@@ -223,7 +223,7 @@ Saad visualiseerida muutujaid kõrvuti Seaborn graafikutega.
 >
 > Logistiline regressioon tugineb 'maksimaalse tõenäosuse' kontseptsioonile, kasutades [sigmoidfunktsioone](https://wikipedia.org/wiki/Sigmoid_function). 'Sigmoidfunktsioon' graafikul näeb välja nagu 'S'-kuju. See võtab väärtuse ja kaardistab selle vahemikku 0 kuni 1. Selle kõverat nimetatakse ka 'logistiliseks kõveraks'. Selle valem näeb välja selline:
 >
-> ![logistiline funktsioon](../../../../translated_images/et/sigmoid.8b7ba9d095c789cf.png)
+> ![logistiline funktsioon](../../../../translated_images/et/sigmoid.8b7ba9d095c789cf.webp)
 >
 > kus sigmoidi keskpunkt asub x-i 0 punktis, L on kõvera maksimaalne väärtus ja k on kõvera järskus. Kui funktsiooni tulemus on suurem kui 0.5, antakse vastavale sildile binaarse valiku klass '1'. Kui mitte, klassifitseeritakse see kui '0'.
 
@@ -372,7 +372,7 @@ plt.show()
 
 Kasutades Matplotlibi, joonista mudeli [Vastuvõtu Tööomaduste Kõver](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) ehk ROC. ROC kõveraid kasutatakse sageli klassifikaatori väljundi vaatamiseks tõeliste ja valepositiivsete osas. "ROC kõveratel on tavaliselt tõeliste positiivsete määr Y-teljel ja valepositiivsete määr X-teljel." Seega on kõvera järskus ja kaugus keskjoonest kõverani olulised: soovid kõverat, mis kiiresti tõuseb ja liigub üle joone. Meie puhul on alguses valepositiivsed, kuid seejärel tõuseb joon korralikult üles ja üle:
 
-![ROC](../../../../translated_images/et/ROC_2.777f20cdfc4988ca.png)
+![ROC](../../../../translated_images/et/ROC_2.777f20cdfc4988ca.webp)
 
 Lõpuks kasuta Scikit-learn'i [`roc_auc_score` API-t](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score), et arvutada tegelik 'Kõvera Alune Pindala' (AUC):
 
diff --git a/translations/et/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/et/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
index 90603f9ca..6efe027aa 100644
--- a/translations/et/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
+++ b/translations/et/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "## Logistilise regressioonimudeli loomine - Õppetund 4\n",
     "\n",
-    "![Logistilise vs. lineaarse regressiooni infograafik](../../../../../../translated_images/et/linear-vs-logistic.ba180bf95e7ee667.png)\n",
+    "![Logistilise vs. lineaarse regressiooni infograafik](../../../../../../translated_images/et/linear-vs-logistic.ba180bf95e7ee667.webp)\n",
     "\n",
     "#### **[Loengu-eelne viktoriin](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
     "\n",
@@ -78,7 +78,7 @@
     "\n",
     "Logistiline regressioon ei paku samu võimalusi kui lineaarregressioon. Esimene annab ennustuse `binaarse kategooria` kohta (\"oranž või mitte oranž\"), samas kui viimane suudab ennustada `jätkuvaid väärtusi`, näiteks arvestades kõrvitsa päritolu ja koristusaega, *kui palju selle hind tõuseb*.\n",
     "\n",
-    "![Infograafik autorilt Dasani Madipalli](../../../../../../translated_images/et/pumpkin-classifier.562771f104ad5436.png)\n",
+    "![Infograafik autorilt Dasani Madipalli](../../../../../../translated_images/et/pumpkin-classifier.562771f104ad5436.webp)\n",
     "\n",
     "### Muud klassifikatsioonid\n",
     "\n",
@@ -88,7 +88,7 @@
     "\n",
     "- **Ordinaalne**, mis hõlmab järjestatud kategooriaid, kasulik, kui tahame tulemusi loogiliselt järjestada, näiteks meie kõrvitsad, mis on järjestatud kindla arvu suuruste järgi (mini, väike, keskmine, suur, väga suur, hiiglaslik).\n",
     "\n",
-    "![Multinomiaalne vs ordinaalne regressioon](../../../../../../translated_images/et/multinomial-vs-ordinal.36701b4850e37d86.png)\n",
+    "![Multinomiaalne vs ordinaalne regressioon](../../../../../../translated_images/et/multinomial-vs-ordinal.36701b4850e37d86.webp)\n",
     "\n",
     "#### **Muutujad EI PEA korreleeruma**\n",
     "\n",
diff --git a/translations/et/2-Regression/README.md b/translations/et/2-Regression/README.md
index b570302fd..c5f47900e 100644
--- a/translations/et/2-Regression/README.md
+++ b/translations/et/2-Regression/README.md
@@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Põhja-Ameerikas nikerdatakse kõrvitsatesse sageli hirmutavaid nägusid Halloweeni jaoks. Uurime lähemalt neid põnevaid köögivilju!
 
-![jack-o-lanterns](../../../translated_images/et/jack-o-lanterns.181c661a9212457d.jpg)
+![jack-o-lanterns](../../../translated_images/et/jack-o-lanterns.181c661a9212457d.webp)
 > Foto autor <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> lehel <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Mida sa õpid
diff --git a/translations/et/3-Web-App/README.md b/translations/et/3-Web-App/README.md
index 701cbc100..85f675e68 100644
--- a/translations/et/3-Web-App/README.md
+++ b/translations/et/3-Web-App/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Selles õppekava osas tutvustatakse teile rakenduslikku masinõppe teemat: kuidas salvestada oma Scikit-learn mudel failina, mida saab kasutada ennustuste tegemiseks veebirakenduses. Kui mudel on salvestatud, õpite, kuidas seda kasutada Flaskis ehitatud veebirakenduses. Kõigepealt loote mudeli, kasutades andmeid, mis käsitlevad UFO-vaatlusi! Seejärel ehitate veebirakenduse, mis võimaldab sisestada sekundite arvu koos laius- ja pikkuskraadi väärtustega, et ennustada, milline riik teatas UFO nägemisest.
 
-![UFO Parkimine](../../../translated_images/et/ufo.9e787f5161da9d4d.jpg)
+![UFO Parkimine](../../../translated_images/et/ufo.9e787f5161da9d4d.webp)
 
 Foto autor <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> lehel <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
diff --git a/translations/et/4-Classification/1-Introduction/README.md b/translations/et/4-Classification/1-Introduction/README.md
index 7766870b2..2e3d86b1d 100644
--- a/translations/et/4-Classification/1-Introduction/README.md
+++ b/translations/et/4-Classification/1-Introduction/README.md
@@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 Nendes neljas õppetunnis uurid klassikalise masinõppe põhivaldkonda - _klassifikatsiooni_. Vaatame erinevate klassifikatsioonialgoritmide kasutamist andmekogumiga, mis käsitleb Aasia ja India suurepäraseid kööke. Loodetavasti oled näljane!
 
-![ainult näpuotsaga!](../../../../translated_images/et/pinch.1b035ec9ba7e0d40.png)
+![ainult näpuotsaga!](../../../../translated_images/et/pinch.1b035ec9ba7e0d40.webp)
 
 > Tähista pan-Aasia kööke nendes õppetundides! Pilt: [Jen Looper](https://twitter.com/jenlooper)
 
@@ -38,7 +38,7 @@ Klassifikatsioon on üks masinõppe teadlase ja andmeteadlase põhitegevusi. Ala
 
 Teaduslikumalt öeldes loob sinu klassifikatsioonimeetod ennustava mudeli, mis võimaldab kaardistada sisendmuutujate ja väljundmuutujate vahelisi seoseid.
 
-![binaarne vs. mitmeklassiline klassifikatsioon](../../../../translated_images/et/binary-multiclass.b56d0c86c81105a6.png)
+![binaarne vs. mitmeklassiline klassifikatsioon](../../../../translated_images/et/binary-multiclass.b56d0c86c81105a6.webp)
 
 > Binaarsed vs. mitmeklassilised probleemid, mida klassifikatsioonialgoritmid peavad lahendama. Infograafika: [Jen Looper](https://twitter.com/jenlooper)
 
@@ -134,7 +134,7 @@ Nüüd muutub töö huvitavamaks. Uurime andmete jaotust köökide kaupa.
     df.cuisine.value_counts().plot.barh()
     ```
 
-    ![köögiandmete jaotus](../../../../translated_images/et/cuisine-dist.d0cc2d551abe5c25.png)
+    ![köögiandmete jaotus](../../../../translated_images/et/cuisine-dist.d0cc2d551abe5c25.webp)
 
     Kööke on piiratud arv, kuid andmete jaotus on ebaühtlane. Saad selle parandada! Enne seda uurime veidi rohkem.
 
@@ -188,7 +188,7 @@ Nüüd saad sügavamale andmetesse kaevuda ja teada saada, millised on tüüpili
     thai_ingredient_df.head(10).plot.barh()
     ```
 
-    ![tai](../../../../translated_images/et/thai.0269dbab2e78bd38.png)
+    ![tai](../../../../translated_images/et/thai.0269dbab2e78bd38.webp)
 
 1. Tee sama jaapani andmete jaoks:
 
@@ -197,7 +197,7 @@ Nüüd saad sügavamale andmetesse kaevuda ja teada saada, millised on tüüpili
     japanese_ingredient_df.head(10).plot.barh()
     ```
 
-    ![jaapani](../../../../translated_images/et/japanese.30260486f2a05c46.png)
+    ![jaapani](../../../../translated_images/et/japanese.30260486f2a05c46.webp)
 
 1. Nüüd hiina koostisosade jaoks:
 
@@ -206,7 +206,7 @@ Nüüd saad sügavamale andmetesse kaevuda ja teada saada, millised on tüüpili
     chinese_ingredient_df.head(10).plot.barh()
     ```
 
-    ![hiina](../../../../translated_images/et/chinese.e62cafa5309f111a.png)
+    ![hiina](../../../../translated_images/et/chinese.e62cafa5309f111a.webp)
 
 1. Joonista india koostisosad:
 
@@ -215,7 +215,7 @@ Nüüd saad sügavamale andmetesse kaevuda ja teada saada, millised on tüüpili
     indian_ingredient_df.head(10).plot.barh()
     ```
 
-    ![india](../../../../translated_images/et/indian.2c4292002af1a1f9.png)
+    ![india](../../../../translated_images/et/indian.2c4292002af1a1f9.webp)
 
 1. Lõpuks joonista korea koostisosad:
 
@@ -224,7 +224,7 @@ Nüüd saad sügavamale andmetesse kaevuda ja teada saada, millised on tüüpili
     korean_ingredient_df.head(10).plot.barh()
     ```
 
-    ![korea](../../../../translated_images/et/korean.4a4f0274f3d9805a.png)
+    ![korea](../../../../translated_images/et/korean.4a4f0274f3d9805a.webp)
 
 1. Nüüd eemalda kõige levinumad koostisosad, mis tekitavad segadust erinevate köökide vahel, kutsudes `drop()`:
 
diff --git a/translations/et/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/et/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
index 6a48ba117..dc71f3208 100644
--- a/translations/et/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
+++ b/translations/et/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -39,7 +39,7 @@
     "Nendes neljas õppetükis uurime klassikalise masinõppe põhivaldkonda - *klassifikatsiooni*. Vaatame, kuidas kasutada erinevaid klassifikatsioonialgoritme, kasutades andmekogumit, mis käsitleb Aasia ja India köögi imelisi roogasid. Loodetavasti olete näljased!\n",
     "\n",
     "<p>\n",
-    "   <img src=\"../../../../../../translated_images/et/pinch.1b035ec9ba7e0d40.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/pinch.1b035ec9ba7e0d40.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Tähistage pan-Aasia kööke nendes õppetundides! Pilt: Jen Looper</figcaption>\n",
     "\n",
@@ -64,7 +64,7 @@
     "Teaduslikumalt öeldes loob teie klassifikatsioonimeetod ennustava mudeli, mis võimaldab teil kaardistada sisendmuutujate ja väljundmuutujate vahelist seost.\n",
     "\n",
     "<p>\n",
-    "   <img src=\"../../../../../../translated_images/et/binary-multiclass.b56d0c86c81105a6.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/binary-multiclass.b56d0c86c81105a6.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Binaarsed vs. mitmeklassilised probleemid, mida klassifikatsioonialgoritmid lahendavad. Infograafika: Jen Looper</figcaption>\n",
     "\n",
@@ -224,7 +224,7 @@
     "> [Tibble](https://tibble.tidyverse.org/) on kaasaegne andmeraamistik.\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/dplyr_filter.b480b264b03439ff.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/dplyr_filter.b480b264b03439ff.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Illustratsioon: @allison_horst</figcaption>\n"
    ],
@@ -514,7 +514,7 @@
     "## Andmete eeltöötlus retseptide abil 👩‍🍳👨‍🍳 - Tasakaalustamata andmete käsitlemine ⚖️\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/recipes.186acfa8ed2e8f00.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/recipes.186acfa8ed2e8f00.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Illustratsioon: @allison_horst</figcaption>\n",
     "\n",
@@ -709,7 +709,7 @@
     "[Cassie Breviu](https://www.twitter.com/cassieview) ja [Jen Looper](https://www.twitter.com/jenlooper) selle mooduli algse Python-versiooni loomise eest ♥️\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/r_learners_sm.cd14eb3581a9f28d.jpeg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/r_learners_sm.cd14eb3581a9f28d.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Illustratsioon: @allison_horst</figcaption>\n"
    ],
diff --git a/translations/et/4-Classification/2-Classifiers-1/README.md b/translations/et/4-Classification/2-Classifiers-1/README.md
index 0b6435b64..ee6c0882e 100644
--- a/translations/et/4-Classification/2-Classifiers-1/README.md
+++ b/translations/et/4-Classification/2-Classifiers-1/README.md
@@ -107,7 +107,7 @@ Scikit-learn liigitab klassifitseerimise juhendatud õppimise alla, ja selles ka
 
 Millist klassifikaatorit valida? Sageli on hea katsetada mitmeid ja otsida parimat tulemust. Scikit-learn pakub [kõrvutavat võrdlust](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) loodud andmestikul, võrreldes KNeighbors, SVC kahte viisi, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB ja QuadraticDiscriminationAnalysis, näidates tulemusi visualiseeritult:
 
-![klassifikaatorite võrdlus](../../../../translated_images/et/comparison.edfab56193a85e7f.png)
+![klassifikaatorite võrdlus](../../../../translated_images/et/comparison.edfab56193a85e7f.webp)
 > Graafikud on genereeritud Scikit-learn'i dokumentatsioonis
 
 > AutoML lahendab selle probleemi elegantselt, tehes need võrdlused pilves ja võimaldades valida parima algoritmi sinu andmete jaoks. Proovi seda [siin](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)
@@ -116,7 +116,7 @@ Millist klassifikaatorit valida? Sageli on hea katsetada mitmeid ja otsida parim
 
 Parem viis kui lihtsalt juhuslikult arvata, on järgida ideid selle allalaaditava [ML Cheat Sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) abil. Siin avastame, et meie mitmeklassi probleemi jaoks on mõned valikud:
 
-![spikker mitmeklassi probleemide jaoks](../../../../translated_images/et/cheatsheet.07a475ea444d2223.png)
+![spikker mitmeklassi probleemide jaoks](../../../../translated_images/et/cheatsheet.07a475ea444d2223.webp)
 > Microsofti algoritmi spikri osa, mis kirjeldab mitmeklassi klassifikatsiooni valikuid
 
 ✅ Laadi see spikker alla, prindi see välja ja riputa seinale!
@@ -147,7 +147,7 @@ Dokumentatsiooni järgi mitmeklassi puhul treeningalgoritm:
 
 Scikit-learn pakub seda tabelit, et selgitada, kuidas solverid käsitlevad erinevaid väljakutseid, mida esitavad erinevat tüüpi andmestruktuurid:
 
-![solverid](../../../../translated_images/et/solvers.5fc648618529e627.png)
+![solverid](../../../../translated_images/et/solvers.5fc648618529e627.webp)
 
 ## Harjutus - andmete jagamine
 
diff --git a/translations/et/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/et/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
index fa71de896..214613670 100644
--- a/translations/et/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
+++ b/translations/et/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -564,7 +564,7 @@
     "## 3. Klassifikaatori valimine\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/parsnip.cd2ce92622976502.jpg\"\n",
+    "   <img src=\"../../../../../../translated_images/et/parsnip.cd2ce92622976502.webp\"\n",
     "   width=\"600\"/>\n",
     "   <figcaption>Kunstiteos: @allison_horst</figcaption>\n"
    ],
@@ -618,7 +618,7 @@
     "Parem viis kui lihtsalt juhuslikult arvata, on järgida ideid sellelt allalaaditavalt [ML spikri lehelt](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Siin avastame, et meie mitmeklassilise probleemi jaoks on mõned valikud:\n",
     "\n",
     "<p >\n",
-    "   <img src=\"../../../../../../translated_images/et/cheatsheet.07a475ea444d2223.png\"\n",
+    "   <img src=\"../../../../../../translated_images/et/cheatsheet.07a475ea444d2223.webp\"\n",
     "   width=\"500\"/>\n",
     "   <figcaption>Microsofti algoritmi spikri lehe osa, mis kirjeldab mitmeklassilise klassifikatsiooni valikuid</figcaption>\n"
    ],