diff --git a/README.md b/README.md
index 358c660313b1ef12d4cfa65f2b4ee9f6cdd9ece1..0d4766e8aa0c3877f1e567a193c1d4132ad6c301 100644
--- a/README.md
+++ b/README.md
@@ -27,14 +27,14 @@ In this curriculum, you will learn:
 
 What we will not cover in this curriculum:
 
-* Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-57639-dmitryso) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-57639-dmitryso), developed in cooperation with [INSEAD](https://www.insead.edu/).
+* Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-cacaste) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste), developed in cooperation with [INSEAD](https://www.insead.edu/).
 * **Classic Machine Learning**, which is well described in our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners)
-* Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-57639-dmitryso)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-57639-dmitryso), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso) and others.
-* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-57639-dmitryso). Consider using [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-57639-dmitryso) and [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-57639-dmitryso) learning paths.
-* **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-57639-dmitryso) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
+* Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-cacaste)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-cacaste), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste) and others.
+* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-cacaste). Consider using [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-cacaste) and [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-cacaste) learning paths.
+* **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-cacaste) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
 * **Deep Mathematics** behind deep learning. For this, we would recommend [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
 
-For a gentle introduction to *AI in the Cloud* topics you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-57639-dmitryso) Learning Path.
+For a gentle introduction to *AI in the Cloud* topics you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) Learning Path.
 
 ---
 # Content
@@ -59,11 +59,11 @@ For a gentle introduction to *AI in the Cloud* topics you may consider taking th
    <td><a href="lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb">Keras</a>/<a href="lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb">TensorFlow</a></td>
    <td><a href="lessons/3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
 <tr><td>IV</td><td><b><a href="lessons/4-ComputerVision/README.md">Computer Vision</a></b></td>
-  <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-57639-dmitryso"><i>AI Fundamentals: Explore Computer Vision</i></a></td>
+  <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-77998-cacaste"><i>AI Fundamentals: Explore Computer Vision</i></a></td>
   <td></td></tr>
 <tr><td></td><td colspan="2"><i>Microsoft Learn Module on Computer Vision</i></td>
-  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
-  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
+  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste"><i>PyTorch</i></a></td>
+  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste"><i>TensorFlow</i></a></td>
   <td></td></tr>
 <tr><td>6</td><td>Intro to Computer Vision. OpenCV</td><td><a href="lessons/4-ComputerVision/06-IntroCV/README.md">Text</a><td colspan="2"><a href="lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb">Notebook</a></td><td><a href="lessons/4-ComputerVision/06-IntroCV/lab/README.md">Lab</a></td></tr>
 <tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="lessons/4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
@@ -73,11 +73,11 @@ For a gentle introduction to *AI in the Cloud* topics you may consider taking th
 <tr><td>11</td><td>Object Detection</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/README.md">Text</a></td><td>PyTorch</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb">TensorFlow</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/lab/README.md">Lab</a></td></tr>
 <tr><td>12</td><td>Semantic Segmentation. U-Net</td><td><a href="lessons/4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
 <tr><td>V</td><td><b><a href="lessons/5-NLP/README.md">Natural Language Processing</a></b></td>
-   <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso"><i>AI Fundamentals: Explore Natural Language Processing</i></a></td>
+   <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste"><i>AI Fundamentals: Explore Natural Language Processing</i></a></td>
    <td></td></tr>
 <tr><td></td><td colspan="2"><i>Microsoft Learn Module on Natural Language</i></td>
-   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
-   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
+   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste"><i>PyTorch</i></a></td>
+   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste"><i>TensorFlow</i></a></td>
    <td></td></tr>
 <tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="lessons/5-NLP/13-TextRep/README.md">Text</a></td><td><a href="lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
 <tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="lessons/5-NLP/14-Embeddings/README.md">Text</td><td><a href="lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
@@ -92,7 +92,7 @@ For a gentle introduction to *AI in the Cloud* topics you may consider taking th
 <tr><td>22</td><td>Deep Reinforcement Learning</td><td><a href="lessons/6-Other/22-DeepRL/README.md">Text</a></td><td></td><td><a href="lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb">TensorFlow</td><td><a href="lessons/6-Other/22-DeepRL/lab/README.md">Lab</a></td></tr>
 <tr><td>23</td><td>Multi-Agent Systems</td><td><a href="lessons/6-Other/23-MultiagentSystems/README.md">Text</a></td><td></td><td></td><td></td></tr>
 <tr><td>VII</td><td colspan="4"><b>AI Ethics</b></td><td></td></tr>
-<tr><td>24</td><td>AI Ethics and Responsible AI</td><td><a href="lessons/7-Ethics/README.md">Text</a></td><td colspan="2"><a href="https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-57639-dmitryso"><i>MS Learn: Responsible AI Principles</i></a></td><td></td></tr>
+<tr><td>24</td><td>AI Ethics and Responsible AI</td><td><a href="lessons/7-Ethics/README.md">Text</a></td><td colspan="2"><a href="https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste"><i>MS Learn: Responsible AI Principles</i></a></td><td></td></tr>
 <tr><td></td><td colspan="4"><b>Extras</b></td><td></td></tr>
 <tr><td>X1</td><td>Multi-Modal Networks, CLIP and VQGAN</td><td><a href="lessons/X-Extras/X1-MultiModal/README.md">Text</a></td><td colspan="2"><a href="lessons/X-Extras/X1-MultiModal/Clip.ipynb">Notebook</a></td><td></td></tr>
 </table>
@@ -120,7 +120,7 @@ However, if you would like to take the course as a self-study project, we sugges
 - Visit the [Discussion board](https://github.com/microsoft/AI-For-Beginners/discussions) to "learn out loud".
 - Chat with other learners [on Gitter](https://gitter.im/Microsoft/ai-for-beginners) or [in Telegram channel](http://t.me/ai_for_beginners).
 
-> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-57639-dmitryso) modules and learning paths.
+> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste) modules and learning paths.
 
 **Teachers**, we have [included some suggestions](/etc/for-teachers.md) on how to use this curriculum.
 
diff --git a/etc/Mindmap.html b/etc/Mindmap.html
index 40d97dfe692dbd940b58c697a9d43445b45949ee..e602e96a3fba34b665da787137a9ce5ea5704cdb 100644
--- a/etc/Mindmap.html
+++ b/etc/Mindmap.html
@@ -20,6 +20,6 @@
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 <body>
 <svg id="mindmap"></svg>
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diff --git a/etc/Mindmap.md b/etc/Mindmap.md
index 69f435b950175542219076b136b323546dfd22cd..134f5130c783688b01e56c22523cc0712fbef528 100644
--- a/etc/Mindmap.md
+++ b/etc/Mindmap.md
@@ -8,7 +8,7 @@
      - Bottom-up/Neural
      - Evolutionary
      - Synergetic / Emergent AI
- - [Microsoft AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-57639-dmitryso)
+ - [Microsoft AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste)
 
 ## [Sybmbolic AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/README.md)
  - Knowledge Representation
@@ -26,9 +26,9 @@
 
 ## [Computer Vision](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/README.md)
  - On MS Learn
-    - [AI Fundamentals: Explore Computer Vision](https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-57639-dmitryso)
-    - [CV with PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso)
-    - [CV with TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso)
+    - [AI Fundamentals: Explore Computer Vision](https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-77998-cacaste)
+    - [CV with PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste)
+    - [CV with TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)
  - [Intro to CV. OpenCV](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/06-IntroCV/README.md)
  - [Convolutional Networks](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/README.md)
    - [CNN Architectures](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md)
@@ -42,9 +42,9 @@
  
 ## [Natural Language Processing](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/README.md)
  - On MS Learn
-    - [AI Fundamentals: Explore NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso)
-    - [NLP with PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso)
-    - [NLP with TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso)
+    - [AI Fundamentals: Explore NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste)
+    - [NLP with PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste)
+    - [NLP with TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste)
  - [Text Representation](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/README.md)
     - Bag of Words
     - TF/IDF
@@ -65,7 +65,7 @@
  - [Multi-Agent Systems](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/23-MultiagentSystems/README.md)
 
 ## [AI Ethics](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/7-Ethics/README.md)
- - [MS Learn on Responsible AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-57639-dmitryso)
+ - [MS Learn on Responsible AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste)
 ## Extras
  - [Multimodal Networks](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/README.md)
    - [CLIP](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/Clip.ipynb)
diff --git a/etc/Mindmap.svg b/etc/Mindmap.svg
index 6d56245a3eef663415a35adec949d2a62061c14c..0985ef25fbe920c53c1e9f5039e10481799a30db 100644
--- a/etc/Mindmap.svg
+++ b/etc/Mindmap.svg
@@ -1 +1 @@
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OpenCV</a></div></foreignObject></g><g data-depth="4" data-path="1.23.24" transform="translate(408, -198.125)" class="markmap-node"><line x1="-1" x2="115" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="113" cy="20" r="6" stroke="rgb(214, 39, 40)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="97" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">On MS Learn</div></foreignObject></g><g data-depth="4" data-path="1.16.19" transform="translate(409, -278.125)" class="markmap-node"><line x1="-1" x2="162" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="160" cy="20" r="6" stroke="rgb(188, 189, 34)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="144" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/README.md">Intro to Frameworks</a></div></foreignObject></g><g data-depth="4" data-path="1.16.18" transform="translate(409, -333.125)" class="markmap-node"><line x1="-1" x2="188" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="170" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/04-OwnFramework/README.md">Multi-Layered Networks</a></div></foreignObject></g><g data-depth="4" data-path="1.16.17" transform="translate(409, -358.125)" class="markmap-node"><line x1="-1" x2="98" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="80" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a 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stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="208" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste">Microsoft AI Business School</a></div></foreignObject></g><g data-depth="4" data-path="1.2.5" transform="translate(411, -560.625)" class="markmap-node"><line x1="-1" x2="141" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="139" cy="20" r="6" stroke="rgb(148, 103, 189)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="123" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Approaches to AI</div></foreignObject></g><g data-depth="4" data-path="1.2.4" transform="translate(411, -585.625)" class="markmap-node"><line x1="-1" x2="106" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="88" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">History of AI</div></foreignObject></g><g data-depth="4" data-path="1.2.3" transform="translate(411, -610.625)" class="markmap-node"><line x1="-1" x2="106" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="88" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">AI Definition</div></foreignObject></g><g data-depth="2" data-path="1.63" transform="translate(193, 531.875)" class="markmap-node"><line x1="-1" x2="65" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="2"></line><circle stroke-width="1.5" cx="63" cy="20" r="6" stroke="rgb(44, 160, 44)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="47" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Extras</div></foreignObject></g><g data-depth="2" data-path="1.61" transform="translate(193, 476.875)" class="markmap-node"><line x1="-1" x2="83" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="2"></line><circle stroke-width="1.5" cx="81" cy="20" r="6" stroke="rgb(31, 119, 180)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="65" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/7-Ethics/README.md">AI Ethics</a></div></foreignObject></g><g data-depth="2" data-path="1.57" transform="translate(193, 421.875)" class="markmap-node"><line x1="-1" x2="145" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="2"></line><circle stroke-width="1.5" cx="143" cy="20" r="6" stroke="rgb(227, 119, 194)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="127" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Other Techniques</div></foreignObject></g><g data-depth="2" data-path="1.38" transform="translate(193, 219.375)" class="markmap-node"><line x1="-1" x2="230" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="2"></line><circle stroke-width="1.5" cx="228" cy="20" r="6" stroke="rgb(127, 127, 127)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="212" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/README.md">Natural Language Processing</a></div></foreignObject></g><g data-depth="2" data-path="1.23" transform="translate(193, -78.125)" class="markmap-node"><line x1="-1" x2="137" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="2"></line><circle stroke-width="1.5" cx="135" cy="20" r="6" stroke="rgb(44, 160, 44)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="119" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/README.md">Computer Vision</a></div></foreignObject></g><g data-depth="2" data-path="1.16" transform="translate(193, -318.125)" class="markmap-node"><line x1="-1" x2="138" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="2"></line><circle stroke-width="1.5" cx="136" cy="20" r="6" stroke="rgb(140, 86, 75)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="120" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/README.md">Neural Networks</a></div></foreignObject></g><g data-depth="2" data-path="1.11" transform="translate(193, -425.625)" class="markmap-node"><line x1="-1" x2="112" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="2"></line><circle stroke-width="1.5" cx="110" cy="20" r="6" stroke="rgb(31, 119, 180)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="94" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/README.md">Sybmbolic AI</a></div></foreignObject></g><g data-depth="2" data-path="1.2" transform="translate(193, -551.875)" class="markmap-node"><line x1="-1" x2="140" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="2"></line><circle stroke-width="1.5" cx="138" cy="20" r="6" stroke="rgb(255, 127, 14)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="122" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><a href="https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md">Introduction to AI</a></div></foreignObject></g><g data-depth="1" data-path="1" transform="translate(80,-10)" class="markmap-node"><line x1="-1" x2="35" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="4"></line><circle stroke-width="1.5" cx="33" cy="20" r="6" stroke="rgb(31, 119, 180)" fill="rgb(255, 255, 255)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="17" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">AI</div></foreignObject></g></g></svg>
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diff --git a/etc/how-to-run.md b/etc/how-to-run.md
index 955009c173b975e1c5a20f996afa103bc408b561..ea791fe0fb59d6c76c96bdad779f07e4d81575c2 100644
--- a/etc/how-to-run.md
+++ b/etc/how-to-run.md
@@ -17,7 +17,7 @@ conda activate ai4beg
 
 ### Using Visual Studio Code with Python Extension
 
-Probably the best way to use the curriculum is to open it in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-57639-dmitryso) with [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-57639-dmitryso).
+Probably the best way to use the curriculum is to open it in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) with [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste).
 
 > **Note**: Once you clone and open the directory in VS Code, it will automatically suggest you to install Python extensions. You would also have to install miniconda as described above.
 
@@ -52,12 +52,12 @@ If you do not want to install Python locally, and have access to some cloud reso
 
 ## Running in the Cloud with GPU
 
-Some of the later lessons in this curriculum would greatly benefit from GPU support, because otherwise training will be painfully slow. There are a few options you can follow, especially if you have access to the cloud either through [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-57639-dmitryso), or through your institution:
+Some of the later lessons in this curriculum would greatly benefit from GPU support, because otherwise training will be painfully slow. There are a few options you can follow, especially if you have access to the cloud either through [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), or through your institution:
 
-* Create [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-57639-dmitryso) and connect to it through Jupyter. You can then clone the repo right onto the machine, and start learning. NC-series VMs have GPU support.
+* Create [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) and connect to it through Jupyter. You can then clone the repo right onto the machine, and start learning. NC-series VMs have GPU support.
 
 > **Note**: Some subscriptions, including Azure for Students, do not provide GPU support out of the box. You may need to request additional GPU cores through technical support request.
 
-* Create [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) and then use Notebook feature there. [This video](https://azure-for-academics.github.io/quickstart/azureml-papers/) shows how to clone a repository into Azure ML notebook and start using it.
+* Create [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) and then use Notebook feature there. [This video](https://azure-for-academics.github.io/quickstart/azureml-papers/) shows how to clone a repository into Azure ML notebook and start using it.
 
 You can also use Google Colab, which comes with some free GPU support, and upload Jupyter Notebooks there to execute them one-by-one.
diff --git a/lessons/2-Symbolic/README.md b/lessons/2-Symbolic/README.md
index 49491f7592b6bb52f4405ec93a8a59fbe726ab16..aefced6d4847071e99c1ac3240b83e26012f5bb5 100644
--- a/lessons/2-Symbolic/README.md
+++ b/lessons/2-Symbolic/README.md
@@ -212,7 +212,7 @@ See [FamilyOntology.ipynb](FamilyOntology.ipynb) for an example of using Semanti
 
 In most of the cases, ontologies are carefully created by hand. However, it is also possible to **mine** ontologies from unstructured data, for example, from natural language texts.
 
-One such attempt was done by Microsoft Research, and resulted in [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-57639-dmitryso).
+One such attempt was done by Microsoft Research, and resulted in [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste).
 
 It is a large collection of entities grouped together using `is-a` inheritance relationship. It allows answering questions like "What is Microsoft?" - the answer being something like "a company with probability 0.87, and a brand with probability 0.75".
 
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/README.md b/lessons/3-NeuralNetworks/03-Perceptron/README.md
index ca1c23a78e6b99040f21634915976ee5c0105cc2..48d9fcb66c5ad1b2175f4a25abf9b4a442030e30 100644
--- a/lessons/3-NeuralNetworks/03-Perceptron/README.md
+++ b/lessons/3-NeuralNetworks/03-Perceptron/README.md
@@ -74,7 +74,7 @@ In this lesson, you learned about a perceptron, which is a binary classification
 
 ## 🚀 Challenge
 
-If you'd like to try to build your own perceptron, try [this lab on Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-57639-dmitryso) which uses the [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-57639-dmitryso).
+If you'd like to try to build your own perceptron, try [this lab on Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-77998-cacaste) which uses the [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-77998-cacaste).
 
 ## [Post-lecture quiz](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/203)
 
diff --git a/lessons/4-ComputerVision/06-IntroCV/README.md b/lessons/4-ComputerVision/06-IntroCV/README.md
index 93a2e702ed57ed2a6fe911ad04cb146bddfaae92..d958437be58898c4a1092567fe2e572ef9862e2e 100644
--- a/lessons/4-ComputerVision/06-IntroCV/README.md
+++ b/lessons/4-ComputerVision/06-IntroCV/README.md
@@ -94,7 +94,7 @@ Sometimes, relatively complex tasks such as movement detection or fingertip dete
 
 ## 🚀 Challenge
 
-Watch [this video](https://docs.microsoft.com/shows/ai-show/ai-show--2021-opencv-ai-competition--grand-prize-winners--cortic-tigers--episode-32?WT.mc_id=academic-57639-dmitryso) from the AI show to learn about the Cortic Tigers project and how they built a block-based solution to democratize computer vision tasks via a robot. Do some research on other projects like this that help onboard new learners into the field.
+Watch [this video](https://docs.microsoft.com/shows/ai-show/ai-show--2021-opencv-ai-competition--grand-prize-winners--cortic-tigers--episode-32?WT.mc_id=academic-77998-cacaste) from the AI show to learn about the Cortic Tigers project and how they built a block-based solution to democratize computer vision tasks via a robot. Do some research on other projects like this that help onboard new learners into the field.
 
 ## [Post-lecture quiz](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/206)
 
diff --git a/lessons/4-ComputerVision/08-TransferLearning/README.md b/lessons/4-ComputerVision/08-TransferLearning/README.md
index 961f396b38f866f1cad0207cc6a031032326844a..4e0f5986647010a7e29e96721a480442cb44b397 100644
--- a/lessons/4-ComputerVision/08-TransferLearning/README.md
+++ b/lessons/4-ComputerVision/08-TransferLearning/README.md
@@ -24,7 +24,7 @@ Here are sample features extracted from a picture of a cat by VGG-16 network:
 
 ## Cats vs. Dogs Dataset
 
-In this example, we will use a dataset of [Cats and Dogs](https://www.microsoft.com/download/details.aspx?id=54765&WT.mc_id=academic-57639-dmitryso), which is very close to a real-life image classification scenario.
+In this example, we will use a dataset of [Cats and Dogs](https://www.microsoft.com/download/details.aspx?id=54765&WT.mc_id=academic-77998-cacaste), which is very close to a real-life image classification scenario.
 
 ## ✍️ Exercise: Transfer Learning
 
diff --git a/lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb b/lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
index 6f3acf02ed486c5927b0d7d73e04227db036e72e..82c97d9b722cb284ae698bef02f2248db7082d83 100644
--- a/lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
+++ b/lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
@@ -31,7 +31,7 @@
    "source": [
     "## Cats vs. Dogs Dataset\n",
     "\n",
-    "In this unit, we will solve a real-life problem of classifying images of cats and dogs. For this reason, we will use [Kaggle Cats vs. Dogs Dataset](https://www.kaggle.com/c/dogs-vs-cats), which can also be downloaded [from Microsoft](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-57639-dmitryso).\n",
+    "In this unit, we will solve a real-life problem of classifying images of cats and dogs. For this reason, we will use [Kaggle Cats vs. Dogs Dataset](https://www.kaggle.com/c/dogs-vs-cats), which can also be downloaded [from Microsoft](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-77998-cacaste).\n",
     "\n",
     "Let's download this dataset and extract it into `data` directory (this process may take some time!):"
    ]
diff --git a/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md
index 16f6dc813271d597884b1898fd15bf4962166944..52217604556933c5e4243603fb7db21923ba4f05 100644
--- a/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md
+++ b/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md
@@ -54,10 +54,10 @@ In this dataset, there is only one class of objects `head`, and for each head, y
 
 You can train an object detection model using one of the following ways:
 
-* Using [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-57639-dmitryso) and it's Python API to programmatically train the model in the cloud. Custom vision will not be able to use more than a few hundred images for training the model, so you may need to limit the dataset.
+* Using [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-77998-cacaste) and it's Python API to programmatically train the model in the cloud. Custom vision will not be able to use more than a few hundred images for training the model, so you may need to limit the dataset.
 * Using the example from [Keras tutorial](https://keras.io/examples/vision/retinanet/) to train RetunaNet model.
 * Using [torchvision.models.detection.RetinaNet](https://pytorch.org/vision/stable/_modules/torchvision/models/detection/retinanet.html) build-in module in torchvision.
 
 ## Takeaway
 
-Object detection is a task that is frequently required in industry. While there are some services that can be used to perform object detection (such as [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-57639-dmitryso)), it is important to understand how object detection works and to be able to train your own models. 
\ No newline at end of file
+Object detection is a task that is frequently required in industry. While there are some services that can be used to perform object detection (such as [Azure Custom Vision](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/object-detection?tabs=visual-studio&WT.mc_id=academic-77998-cacaste)), it is important to understand how object detection works and to be able to train your own models. 
\ No newline at end of file
diff --git a/lessons/5-NLP/13-TextRep/README.md b/lessons/5-NLP/13-TextRep/README.md
index d73673e50f7d0526b0c118dd364b9e4223d67edd..7bd63e7c6dd6742fb231c45a5b989d1f9764a23c 100644
--- a/lessons/5-NLP/13-TextRep/README.md
+++ b/lessons/5-NLP/13-TextRep/README.md
@@ -68,6 +68,6 @@ Try some other exercises using bag-of-words and different data models. You might
 
 ## Review & Self Study
 
-Practice your skills with text embeddings and bag-of-words techniques on [Microsoft Learn](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso)
+Practice your skills with text embeddings and bag-of-words techniques on [Microsoft Learn](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste)
 
 ## [Assignment: Notebooks](assignment.md)
diff --git a/lessons/5-NLP/17-GenerativeNetworks/README.md b/lessons/5-NLP/17-GenerativeNetworks/README.md
index 06798a68fa9403b24c0e4850d5bc4431790e1a13..f6110990a567d1dd7b71b9dd758561a20f3a4652 100644
--- a/lessons/5-NLP/17-GenerativeNetworks/README.md
+++ b/lessons/5-NLP/17-GenerativeNetworks/README.md
@@ -60,7 +60,7 @@ While text generation may be useful in its own right, the major benefits come fr
 
 Take some lessons on Microsoft Learn on this topic
 
-* Text Generation with [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/6-generative-networks/?WT.mc_id=academic-15963-dmitryso)/[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/5-generative-networks/?WT.mc_id=academic-15963-dmitryso)
+* Text Generation with [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/6-generative-networks/?WT.mc_id=academic-77998-cacaste)/[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/5-generative-networks/?WT.mc_id=academic-77998-cacaste)
 
 ## [Post-lecture quiz](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/217)
 
diff --git a/lessons/5-NLP/20-LangModels/README.md b/lessons/5-NLP/20-LangModels/README.md
index 48f624771145d640cb00a87fe89b217075ca9d1c..7189e2470a71a6650284a23f59cdc0a539d22874 100644
--- a/lessons/5-NLP/20-LangModels/README.md
+++ b/lessons/5-NLP/20-LangModels/README.md
@@ -23,7 +23,7 @@ $$
 
 ## GPT is a Family
 
-GPT is not a single model, but rather a collection of models developed and trained by [OpenAI](http://openai.org). The latest model openly available is [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2#openai-gpt2), which has up to 1.5 billion parameters (there are several variations of the model, so you can select one for your tasks that is a good compromise between size/performance). Latest GPT-3 model has up to 175 billion parameters, and is available [as a cognitive service from Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/openai-service/#overview?WT.mc_id=academic-57639-dmitryso), and as [OpenAI API](https://openai.com/api/).
+GPT is not a single model, but rather a collection of models developed and trained by [OpenAI](http://openai.org). The latest model openly available is [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2#openai-gpt2), which has up to 1.5 billion parameters (there are several variations of the model, so you can select one for your tasks that is a good compromise between size/performance). Latest GPT-3 model has up to 175 billion parameters, and is available [as a cognitive service from Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/openai-service/#overview?WT.mc_id=academic-77998-cacaste), and as [OpenAI API](https://openai.com/api/).
 
 ## Prompt-based Inference
 
diff --git a/lessons/5-NLP/README.md b/lessons/5-NLP/README.md
index 725ce85893125d89d73fa9e9b569391d8f232ea9..6fd834e9549b58d8ada18142408f178df91d9f38 100644
--- a/lessons/5-NLP/README.md
+++ b/lessons/5-NLP/README.md
@@ -35,7 +35,7 @@ pip install -r requirements-torch.txt
 pip install -r requirements-tf.txt
 ```
 
-> You can try NLP with TensorFlow on [Microsoft Learn](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/?WT.mc_id=academic-57639-dmitryso)
+> You can try NLP with TensorFlow on [Microsoft Learn](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/?WT.mc_id=academic-77998-cacaste)
 
 ## GPU Warning
 
diff --git a/lessons/6-Other/22-DeepRL/README.md b/lessons/6-Other/22-DeepRL/README.md
index f5c631d0579d1bb86b3c0e366215cf18d1113e70..fa6755dc3cddde4f28f868735b576e98687b5174 100644
--- a/lessons/6-Other/22-DeepRL/README.md
+++ b/lessons/6-Other/22-DeepRL/README.md
@@ -90,7 +90,7 @@ Reinforcement Learning nowadays is a fast growing field of research. Some of the
 
 * Teaching a computer to play **Atari Games**. The challenging part in this problem is that we do not have simple state represented as a vector, but rather a screenshot - and we need to use the CNN to convert this screen image to a feature vector, or to extract reward information. Atari games are available in the Gym.
 * Teaching a computer to play board games, such as Chess and Go. Recently state-of-the-art programs like **Alpha Zero** were trained from scratch by two agents playing against each other, and improving at each step.
-* In industry, RL is used to create control systems from simulation. A service called [Bonsai](https://azure.microsoft.com/services/project-bonsai/?WT.mc_id=academic-57639-dmitryso) is specifically designed for that.
+* In industry, RL is used to create control systems from simulation. A service called [Bonsai](https://azure.microsoft.com/services/project-bonsai/?WT.mc_id=academic-77998-cacaste) is specifically designed for that.
 
 ## Conclusion
 
diff --git a/lessons/7-Ethics/README.md b/lessons/7-Ethics/README.md
index 9a6a7278ffc29470564da5c1609401714b353e78..247eba48b0ecc660a85abed968c919501c8d9b91 100644
--- a/lessons/7-Ethics/README.md
+++ b/lessons/7-Ethics/README.md
@@ -10,7 +10,7 @@ The kind of AI that we have learned about in this course is nothing more than la
 
 ## Principles of Responsible AI
 
-To avoid this accidental or purposeful misuse of AI, Microsoft states the important [Principles of Responsible AI](https://www.microsoft.com/ai/responsible-ai?WT.mc_id=academic-57639-dmitryso). The following concepts underpin these principles:
+To avoid this accidental or purposeful misuse of AI, Microsoft states the important [Principles of Responsible AI](https://www.microsoft.com/ai/responsible-ai?WT.mc_id=academic-77998-cacaste). The following concepts underpin these principles:
 
 * **Fairness** is related to the important problem of *model biases*, which can be caused by using biased data for training. For example, when we try to predict the probability of getting a software developer job for a person, the model is likely to give higher preference to males - just because the training dataset was likely biased towards a male audience. We need to carefully balance training data and investigate the model to avoid biases, and make sure that the model takes into account more relevant features.
 * **Reliability and Safety**. By their nature, AI models can make mistakes. A neural network returns probabilities, and we need to take it into account when making decisions. Every model has some precision and recall, and we need to understand that to prevent harm that wrong advice can cause.
@@ -31,10 +31,10 @@ Microsoft has developed the [Responsible AI Toolbox](https://github.com/microsof
    - EconML - tool for Causal Analysis, which focuses on what-if questions
    - DiCE - tool for Counterfactual Analysis allows you to see which features need to be changed to affect the decision of the model
 
-For more information about AI Ethics, please visit [this lesson](https://github.com/microsoft/ML-For-Beginners/tree/main/1-Introduction/3-fairness?WT.mc_id=academic-57639-dmitryso) on the Machine Learning Curriculum which includes assignments.
+For more information about AI Ethics, please visit [this lesson](https://github.com/microsoft/ML-For-Beginners/tree/main/1-Introduction/3-fairness?WT.mc_id=academic-77998-cacaste) on the Machine Learning Curriculum which includes assignments.
 
 ## Review & Self Study
 
-Take this [Learn Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-57639-dmitryso) to learn more about responsible AI.
+Take this [Learn Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77998-cacaste) to learn more about responsible AI.
 
 ## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
diff --git a/translations/README.ja.md b/translations/README.ja.md
index ff0adbfb35f913c01974cc46082ec3c4a329979f..7d2f45b1ca7208cdb7aa4a333899998966bb1159 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -27,14 +27,14 @@
 
 本カリキュラムで扱わない内容
 
-* **AIをビジネスで活用するためのビジネスケース**。Microsoft Learnの学習パス[ビジネスユーザーのためのAI入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-57639-dmitryso)や、[INSEAD](https://www.insead.edu/)と共同で開発した[AIビジネススクール](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-57639-dmitryso)の受講をご検討ください。
+* **AIをビジネスで活用するためのビジネスケース**。Microsoft Learnの学習パス[ビジネスユーザーのためのAI入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-cacaste)や、[INSEAD](https://www.insead.edu/)と共同で開発した[AIビジネススクール](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste)の受講をご検討ください。
 * **古典的な機械学習**については、[初心者のための機械学習カリキュラム](http://github.com/Microsoft/ML-for-Beginners)で十分に説明されています。
-* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-57639-dmitryso)** を利用して構築された実践的なAIアプリケーション。これには、Microsoft Learnの[ビジョン](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-57639-dmitryso)、[自然言語処理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso)などのモジュールから始めることをお勧めします。
-* [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) や [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-57639-dmitryso) などの特定のML **Cloud Frameworks** を利用する [Azure Machine Learning による機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-57639-dmitryso)、[Azure Databricksによる機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-57639-dmitryso) の学習パスの利用を検討します。
-* **会話型AI**と**Chat Bots**。別途、[Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-57639-dmitryso) という学習パスがあり、詳しくは[こちらのブログ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) も参照してください。
+* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-cacaste)** を利用して構築された実践的なAIアプリケーション。これには、Microsoft Learnの[ビジョン](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-cacaste)、[自然言語処理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste)などのモジュールから始めることをお勧めします。
+* [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) や [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-cacaste) などの特定のML **Cloud Frameworks** を利用する [Azure Machine Learning による機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-cacaste)、[Azure Databricksによる機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-cacaste) の学習パスの利用を検討します。
+* **会話型AI**と**Chat Bots**。別途、[Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-cacaste) という学習パスがあり、詳しくは[こちらのブログ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) も参照してください。
 * **ディープラーニングの背後にある深層数学**。これについては、Ian Goodfellow、Yoshua Bengio、Aaron Courvilleによる[Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)をお勧めします。また、[https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) で公開されています。
 
-クラウドにおける*AI*のトピックを優しく紹介するために、[Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-57639-dmitryso) Learning Pathの受講を検討してもよいでしょう。
+クラウドにおける*AI*のトピックを優しく紹介するために、[Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) Learning Pathの受講を検討してもよいでしょう。
 
 ---
 # コンテンツ
@@ -59,11 +59,11 @@
    <td><a href="../lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td>
    <td><a href="../lessons/3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
 <tr><td>IV</td><td><b><a href="../lessons/4-ComputerVision/README.md">コンピュータビジョン</a></b></td>
-  <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-57639-dmitryso"><i>AIファンダメンタルズ コンピュータビジョンの探求</i></a></td>
+  <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-77998-cacaste"><i>AIファンダメンタルズ コンピュータビジョンの探求</i></a></td>
   <td></td></tr>
 <tr><td></td><td colspan="2"><i>Microsoft Learn Module - コンピュータビジョン</i></td>
-  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
-  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
+  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste"><i>PyTorch</i></a></td>
+  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste"><i>TensorFlow</i></a></td>
   <td></td></tr>
 <tr><td>6</td><td>コンピュータビジョン入門 OpenCV</td><td><a href="../lessons/4-ComputerVision/06-IntroCV/README.md">Text</a><td colspan="2"><a href="../lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb">Notebook</a></td><td><a href="../lessons/4-ComputerVision/06-IntroCV/lab/README.md">Lab</a></td></tr>
 <tr><td>7</td><td>畳み込みニューラルネットワーク<br/>CNN アーキテクチャ</td><td><a href="../lessons/4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="../lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="../lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="../lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="../lessons/4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
@@ -73,11 +73,11 @@
 <tr><td>11</td><td>オブジェクト検出</td><td><a href="../lessons/4-ComputerVision/11-ObjectDetection/README.md">Text</a></td><td>PyTorch</td><td><a href="../lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection-TF.ipynb">TensorFlow</td><td><a href="../lessons/4-ComputerVision/11-ObjectDetection/lab/README.md">Lab</a></td></tr>
 <tr><td>12</td><td>セマンティック・セグメンテーション U-Net</td><td><a href="../lessons/4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="../lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="../lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
 <tr><td>V</td><td><b><a href="../lessons/5-NLP/README.md">自然言語処理</a></b></td>
-   <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso"><i>AIファンダメンタルズ 自然言語処理の探究</i></a></td>
+   <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste"><i>AIファンダメンタルズ 自然言語処理の探究</i></a></td>
    <td></td></tr>
 <tr><td></td><td colspan="2"><i>Microsoft Learn Module - 自然言語</i></td>
-   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
-   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
+   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste"><i>PyTorch</i></a></td>
+   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste"><i>TensorFlow</i></a></td>
    <td></td></tr>
 <tr><td>13</td><td>文書表現 Bow/TF-IDF</td><td><a href="../lessons/5-NLP/13-TextRep/README.md">Text</a></td><td><a href="../lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="../lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
 <tr><td>14</td><td>セマンティックな単語の埋め込み Word2Vec と GloVe</td><td><a href="../lessons/5-NLP/14-Embeddings/README.md">Text</td><td><a href="../lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="../lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
@@ -92,7 +92,7 @@
 <tr><td>22</td><td>深層強化学習</td><td><a href="../lessons/6-Other/22-DeepRL/README.md">Text</a></td><td></td><td><a href="../lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb">TensorFlow</td><td><a href="../lessons/6-Other/22-DeepRL/lab/README.md">Lab</a></td></tr>
 <tr><td>23</td><td>マルチエージェントシステム</td><td><a href="../lessons/6-Other/23-MultiagentSystems/README.md">Text</a></td><td></td><td></td><td></td></tr>
 <tr><td>VII</td><td colspan="4"><b>AI倫理</b></td><td></td></tr>
-<tr><td>24</td><td>AI 倫理と責任ある AI のあり方</td><td><a href="../lessons/7-Ethics/README.md">Text</a></td><td colspan="2"><a href="https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-57639-dmitryso"><i>MS Learn: Responsible AI Principles</i></a></td><td></td></tr>
+<tr><td>24</td><td>AI 倫理と責任ある AI のあり方</td><td><a href="../lessons/7-Ethics/README.md">Text</a></td><td colspan="2"><a href="https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste"><i>MS Learn: Responsible AI Principles</i></a></td><td></td></tr>
 <tr><td></td><td colspan="4"><b>Extras</b></td><td></td></tr>
 <tr><td>X1</td><td>マルチモーダルネットワーク、CLIP、VQGAN</td><td><a href="../lessons/X-Extras/X1-MultiModal/README.md">Text</a></td><td colspan="2"><a href="../lessons/X-Extras/X1-MultiModal/Clip.ipynb">Notebook</a></td><td></td></tr>
 </table>
@@ -121,7 +121,7 @@
 - [議論ボード](https://github.com/microsoft/AI-For-Beginners/discussions)にアクセスして「大きく声を出して」学ぼう
 - 他の学習者と [Gitter](https://gitter.im/Microsoft/ai-for-beginners) または [Telegram チャンネル](http://t.me/ai_for_beginners)でチャットすることができます。
 
-> さらに学習を進めるには、以下の [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-57639-dmitryso) のモジュールとラーニングパスに沿って学習することをお勧めします。
+> さらに学習を進めるには、以下の [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste) のモジュールとラーニングパスに沿って学習することをお勧めします。
 
 **先生方**、このカリキュラムをどのように使うかのいくつかの提案](/etc/for-teachers.md)を参考にしてください。