Welcome to the Microsoft Learn community
Welcome to the official community and blog for Microsoft Learn! Microsoft Learn offers a comprehensive collection of training options that empower technical professionals to learn in a style that fits best, allowing you to advance your technical skills while earning achievements and/or preparing for Microsoft Certifications. Join us for updates on training and certification news as well as conversation with the community around learning, training, and certification! This is not the appropriate place for product questions. Do you have questions about Microsoft Teams, Excel, Azure, Windows 10, or another product? Check out our list of communities and you're bound to find the appropriate community to ask your question. This is also not the place for certification support. Please contact Certification Support if you are having issues with registering for an exam, profile issues, missing certification etc. If you're looking to ask a question or start a conversation about Microsoft Learn, which paths to take, what certification to explore next, you're in the right place! Ask away.Go Beyond the Microsoft Learn AI Skills Challenge
Connect with a network of experts who offer unique knowledge to help you achieve your AI-skilling goals. The Microsoft Learn Community is your go-to destination for advancing your AI skills, whether you're a novice or a seasoned professional looking to deepen your knowledge. Our community offers a dynamic learning environment through Learning Rooms, where you can interact directly with Microsoft Learn experts who are dedicated to the community. Engage in deep dives on specific topics, participate in discussions, and attend virtual sessions to enhance your AI expertise. Our community leaders are passionate about sharing their extensive knowledge and experience with Microsoft products, offering targeted guidance and support within the Learning Rooms. Take your AI journey to the next level with the Microsoft Learn Community and join one of the upcoming sessions. Note: This list reflects sessions each week during the AI Skilling Challenge and will update weekly to showcase the next set of sessions. To see the full list, join our Learner Central AISC session channel. Pay attention to time zones (Time Zone Converter – Time Difference Calculator (timeanddate.com) Topic Area Session Date Session Title & Link Learn Expert AI April 15th, 3-4:00 PM (BRT) How Much Does an AI Project Typically Cost? Jorge Maia AI April 15th, 3-4:00 PM (BRT) Quanto Custa um Projeto de IA? (PT-BR Supported) Jorge Maia Azure AI Fundamentals April 15th, 6-7:00 PM (CET) Four Steps To The AI World (session 4) Saeid Dahl Azure AI Fundamentals April 15th, 6-7:00 PM (CET) AI Skills Challenges - weekly guide (session 4) Saeid Dahl AI April 16th, 8-9:00 PM (AEST) Let's build our own copilots with Generative AI Arafat Tehsin Azure AI April 16th, 3-5:00 PM (JST) Learn how to leverage Azure AI with Power Platform Service.(Power Platform サービスで Azure AI サービスを活用する方法を学ぶ) JPN Supported Shinichi Kawara AI April 17th, 3-4:00 PM (BRT) Is AI Applicable to Any business or problem? Jorge Maia AI April 17th, 3-4:00 PM (BRT) A IA é Aplicável a Qualquer Negócio ou Problema? (PT-BR Supported) Jorge Maia AI April 18th, 6:30-7:30 PM (BRT) Bem-vindo à Era das Soluções “Não Tão Inteligentes” com a IA (In-Person Only) Jorge Maia AI April 19th, 3-4:00 PM (BRT) Welcome to the Era of Not-So-Intelligent Solutions with AI Jorge Maia Azure OpenAI April 20th, 6-7:00 PM (CET) Whispering to OpenAI Saeid Dahl AI, Business Apps & Data April 20th, 1-2:00 PM (GMT +1) Apply & Deploy – AI Hack You Doher Drizzle Pablo AI, Business Apps & Data April 27th, 1-3:00 PM (GMT +1) Complete – AI Elevation: Illuminating the Path Forward Doher Drizzle Pablo AI & Azure April 30th, (CET) Microsoft AI and Azure Credentials: Building Blocks for Success Hamid Sadeghpour Saleh Join Learner Central Learner Central is your all-in-one hub for the latest happenings, opportunities, and resources in the Microsoft Learn Community – all from a single spot – regardless of which learning rooms you’ve joined. Connect with a worldwide network of experts with technical and training experience, who can offer unique knowledge to help you achieve your skilling goals. Join Now Microsoft Learn AI Skills Challenge Whether you’re a seasoned technology professional or are just starting out, this month-long immersive challenge will help you gain the skills, confidence, and Microsoft Credentials needed to excel in the era of AI. Explore AI Fundamentals, Azure Open AI, Machine Learning and Microsoft Fabric. Each challenge features a selection of resources to help you succeed, including interactive Microsoft Learn community events, live and recorded learning sessions delivered by AI experts, plus training assets. By completing one of the challenges, you can become eligible for a free Microsoft Certification exam. Register NowLet’s Learn Together with the Microsoft Learn Community
Connect with our community of Learn experts who offer unique knowledge to help you achieve your AI-skilling goals. The Microsoft Learn Community is your go-to destination for advancing your AI skills, whether you're a novice or a seasoned professional looking to deepen your knowledge. Our community offers a dynamic learning environment through Learning Rooms, where you can interact directly with Microsoft Learn experts who are dedicated to the community. Engage in deep dives on specific topics, participate in discussions, and attend virtual sessions to enhance your AI expertise. Our community leaders are passionate about sharing their extensive knowledge and experience with Microsoft products, offering targeted guidance and support within the Learning Rooms. Take your AI journey to the next level with the Microsoft Learn Community and join one of the upcoming sessions. Note: This list reflects sessions each week during the AI Skilling Challenge and will update weekly to showcase the next set of sessions. To see the full list, join our Learner Central learning room to below access AISC session channel. Pay attention to time zones (Time Zone Converter – Time Difference Calculator (timeanddate.com) Topic Area Session Date Session Title & Link Learn Expert Fabric October 5th, 2024 8 AM Pacific #FabricCoffee with Jared Kuehn: Microsoft Fabric Architecture - Lessons from Year 1 Mehrdad Abdollahi Data and AI October 8th, 2024 10AM Pacific Your Learning Toolkit from Microsoft - Learn it your way Armando Lacerda Azure October 10th, 2024 8-9AM GMT +13 Deploy Cloud-Native Apps using Azure Container Apps Luke Murray Fabric October 10th, 2024 8 AM Pacific Master the Applied Skill: Implement a Lakehouse in Microsoft Fabric! Mehrdad Abdollahi Fabric October 12th, 2024 8 AM GST #FabricCoffee with Surya Teja Josyula - Reimagine Real-Time Intelligence with Microsoft Fabric Mehrdad Abdollahi Data and AI October 15th, 2024 3:30-4:30 PM GMT-3 My AI Learning path to success with Microsoft: Certifications, Skills, and Community Jorge Maia Azure October 16th, 2024 9-10 AM Pacific YouTube - Microsoft Learn Expert Session: AI-Assisted Cloud Management using Microsoft Copilot for Azure LinkedIn - AI-Assisted Cloud Management using Microsoft Copilot for Azure Jonah Andersson Microsoft Applied Skills October 19th, 2024 5:15 PM GMT+1 Enhancing Upskilling; Improving Practical Application Skills for Success in the Real World: Linking Theory and Practice" Oluwaseyi Oluwawumiju Fabric October 19th, 2024 8 AM Pacific #FabricCoffee with Warwick Rudd and Ginger Grant: Developing with Spark for Microsoft Fabric with Copilot Mehrdad Abdollahi Data and AI October 21st, 2024 3:30 - 4:00 PM GMT-3 Building a Knowledge-Powered AI Agent with OpenAI and Your Data Jorge Maia Data and AI October 23rd, 2024 3:30-4:00 PM GMT-3 Accelerating Computer Vision with IoT and Azure: From Idea to Prototype in Days Jorge Maia Azure OpenAI October 24th, 2024 3:30-4:30 PM GMT-3 Como estamos Criando um Sistema de Agentes de IA com IoT e Digital Twins (Br-PT) Jorge Maia Data and AI October 25th, 2024 3:30-4:30 PM GMT -3 Study Case: Join me on my learning path to creating a Multiple-Agent System using IoT and Digital Twins Solutions Jorge Maia Fabric October 26th, 2024 8 AM Pacific #FabricCoffee with Nikola Ilic: 50 Shades of Direct Lake Mehrdad Abdollahi Data and AI October 27th, 2024 3:30-4:00 PM GMT-3 Applied Skills - Creating Intelligent Document Solutions with Azure AI Jorge Maia Copilot October 27th, 2024 3:30-4:30 PM IST Mastering Copilot in Business Central Dr. Gomathi Srinivasan Fabric October 28th, 2024 8AM Pacific Data Architectures and Microsoft Fabric Mehrdad Abdollahi Azure October 30th, 9-10 AM Pacific YouTube - Microsoft Azure AI Fundamentals (AI-900): Study Tips and Exam Preparation Guide LinkedIn - Azure AI Fundamentals (AI-900): Study Tips and Exam Prep Guide Jonah Andersson Data and AI October 31st, 2024 2:30-3:00 PM GMT-2 Applied Skills - Building Natural Language Solutions with Azure AI Jorge Maia Fabric Session Details Upcoming Fabric Applied Skills (Session Link Upcoming) Ashraf Ghonaim Join Learner Central Learner Central is your all-in-one hub for the latest happenings, opportunities, and resources in the Microsoft Learn Community – all from a single spot – regardless of which learning rooms you’ve joined. Connect with a worldwide network of experts with technical and training experience, who can offer unique knowledge to help you achieve your skilling goals. Join Now Microsoft Learn AI Skills Challenge Whether you’re a seasoned technology professional or are just starting out, this month-long immersive challenge will help you gain the skills, confidence, and Microsoft Credentials needed to excel in the era of AI. This is your opportunity to accelerate your AI skills and earn a Microsoft Credential. With six challenges covering AI, Security, Copilot (GitHub, M365, Power Platform), and Microsoft Fabric, you’ll have access to an extensive suite of resources to guide you on your AI journey. Ready to take on the challenge?
Econometrics models vs machine learning algorithms
Econometrics models and machine learning algorithms are used in data analysis, but they have different approaches and are often applied in distinct contexts. Here's a comparison between econometrics models and machine learning algorithms Econometrics Models: Purpose: Econometrics models are primarily used in economics to study and quantify relationships between economic variables. They are designed to explain and predict economic phenomena based on theoretical and statistical principles. Underlying Assumptions: Econometrics models often rely on strong assumptions about the underlying economic structure. These assumptions are derived from economic theories and may include concepts like linearity, homoscedasticity, and normality. Interpretability: Econometric models are typically designed to be interpretable. The coefficients in these models often have clear economic interpretations, allowing researchers to make sense of the relationships between variables. Causal Inference: Econometrics places a strong emphasis on causal inference. Researchers often aim to establish causal relationships between variables, which involves controlling for potential confounding factors. Data Size: Econometric models have traditionally been applied to smaller datasets, and they are often used when the researcher has a strong theoretical basis for the relationships being studied. Machine Learning Algorithms: Purpose: Machine learning algorithms are used for a broader range of tasks, including prediction, classification, clustering, and pattern recognition. They are applied in various domains beyond economics, such as computer vision, natural language processing, and healthcare. Underlying Assumptions: Machine learning algorithms are less concerned with making strong assumptions about the underlying data distribution or structure. They focus more on learning patterns and relationships directly from the data. Interpretability: Many machine learning algorithms, especially complex ones like deep neural networks, are considered "black-box" models. While efforts are being made to improve interpretability, understanding the internal workings of these models can be challenging. Causal Inference: While some machine learning algorithms can estimate causal relationships, they are often used more for prediction rather than explicitly establishing causation. Techniques like feature importance can provide insights into predictive factors but may not imply causation. Data Size: Machine learning algorithms, especially deep learning models, thrive on large datasets. They can automatically learn complex patterns and relationships from vast amounts of data, which is particularly advantageous in tasks like image recognition and natural language processing. Choosing Between Econometrics Models and Machine Learning Algorithms: Nature of the Problem: Consider the nature of the problem you are trying to solve. If you are interested in understanding causal relationships in economic phenomena and have a solid theoretical foundation, econometrics models may be more appropriate. If the focus is on prediction and dealing with large datasets, machine learning algorithms might be more suitable. Interpretability vs. Performance: If interpretability is crucial and you need to understand the economic mechanisms at play, econometrics models may be preferred. If maximizing predictive performance is the primary goal and interpretability is less critical, machine learning algorithms might be more suitable. Data Availability: The amount and nature of your data can influence the choice between econometrics and machine learning. Econometrics models may be more suitable for smaller datasets with a strong theoretical basis, while machine learning algorithms can excel with large, complex datasets. In practice, there is a growing trend of combining elements from both approaches. Some researchers and practitioners leverage econometric models for causal inference and combine them with machine learning techniques for prediction, creating a hybrid approach that benefits from the strengths of both methodologies. Integration between Econometrics Models and Machine Learning Algorithms: Integrating econometrics models with machine learning algorithms can leverage the strengths of both approaches, allowing for more robust analyses that encompass both causal inference and predictive modeling. Here are some ways in which integration can be achieved: Hybrid Models: Create hybrid models that combine elements of econometrics and machine learning. For example, you could use an econometric model to capture the causal relationships in the data and then incorporate machine learning algorithms to improve predictive accuracy. This approach is often referred to as "econometric-ML hybrid models." Feature Engineering: Use machine learning techniques for feature engineering to enhance the set of variables available for your econometric model. Machine learning can identify complex patterns and relationships in the data, which can then be used as additional features in an econometric model. Variable Selection: Apply machine learning algorithms for variable selection. Some machine learning techniques, such as LASSO (Least Absolute Shrinkage and Selection Operator) or recursive feature elimination, can help identify the most relevant variables for your econometric model. Predictive Analytics with Causal Variables: Integrate causal variables identified in econometric models into machine learning algorithms for predictive analytics. This way, you can incorporate the insights gained from the econometric analysis into the machine learning model. Counterfactual Analysis: Use machine learning to simulate counterfactual scenarios. Econometric models often deal with counterfactual analysis, and machine learning models can assist in generating scenarios or predictions in the absence of specific interventions or changes. Ensemble Methods: Employ ensemble methods that combine predictions from both econometric models and machine learning models. This can be done by averaging or combining the predictions, taking advantage of the complementary strengths of each model. Sequential Modeling: Implement a sequential modeling approach where you first estimate an econometric model and then use the residuals or other relevant information as inputs to a machine learning model. This sequential approach allows you to capture both the linear relationships emphasized in econometrics and the non-linear patterns detected by machine learning. Model Validation and Sensitivity Analysis: Use machine learning techniques for model validation and sensitivity analysis of econometric models. Machine learning can help assess the robustness of econometric results under different conditions and identify potential sources of bias or uncertainty. Time Series Analysis: Combine econometric time series models with machine learning techniques for time series forecasting. Machine learning algorithms, such as recurrent neural networks or Long Short-Term Memory (LSTM) networks, can capture temporal dependencies and patterns in the data that traditional econometric time series models may miss. Transfer Learning: Apply transfer learning techniques to leverage knowledge gained from one domain (e.g., econometrics) and transfer it to improve the performance of a machine learning model in a related domain. Successful integration requires a deep understanding of both econometrics and machine learning, as well as the specific characteristics of the data and problem at hand. It's essential to carefully validate and interpret the results of integrated models to ensure that the combined approach adds value to the analysis.
My Microsoft VS Code v1.79 isn't detecting .NET SDK 7.0
I need help. My Microsoft VS Code isn't detecting .NET SDK, and I am in need of it detecting it so that I can continue a module that teaches me the basics of VS Code. I've tried installing different versions of .NET SDK and Microsoft VS Code, but none were working! I need help immediately so that I can be able to continue learning on Microsoft Learn. Note: I'm sorry if this is not the right place to talk about this; I'm still new here.
Make the smart move! Add intelligence to your apps with Azure OpenAI Service
Calling all intermediate-level AI engineers and developers! If you’re ready to add intelligence to your apps, I highly recommend the Develop AI solutions with Azure OpenAI learning path on Microsoft Learn. Azure OpenAI Service brings generative AI models to the Azure platform, enabling you to develop powerful AI solutions that benefit from the security, scalability, and integration of other services provided by Azure. In this learning path, explore and test generative AI models, and build natural language solutions to understand, converse, and generate content. Plus, this learning path helps you prepare for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution to earn the Microsoft Certified: Azure AI Engineer Associate certification. Make the smart move! Find out how to build an OpenAI Service solution, as you #LearnMicrosoftAI.If you have AI on the brAIn, don’t miss our daily #LearnMicrosoftAI tips!
We’re all aware of the amazing abilities of AI, but many of us wonder how to make the most of it in our daily tasks—to improve customer experiences, increase productivity, and drive results. Microsoft AI offers these possibilities, but where do you start? Right here! Beginning on June 5, 2023, stay tuned here in the Microsoft Learn hub on Tech Community for a daily #LearnMicrosoftAI tip. In these tips, community members will provide practical AI skilling advice, including “Soar to new AI developer heights with GitHub Copilot” and “Flex your apps with AI Builder,” to name just a few. Whether you’re a tech beginner or you have years of experience in the field, you’ll find something new to learn. These tips will cover a range of technologies and tools, like Azure OpenAI Service, Azure Machine Learning, AI Builder, Power Virtual Agents, GitHub Copilot, and more. They’ll also include various resources from Microsoft Learn, such as Microsoft Certifications, learning paths and documentation, instructor-led training, and recorded shows. We’ll share an AI skilling tip every weekday— find them on Microsoft Tech Community discussions using the label Learn Microsoft AI filter or search using #LearnMicrosoftAI. Did you miss one tip? Stay tuned for weekly recaps every Friday on Twitter and LinkedIn.
AI-900 Sessions - #LearnMicrosoftAI
As part of #LearnMicrosoftAI campaign, I am happy to share with everyone that my internal session for the Microsoft AI-900 exam has created a positive vibe among the engineers at my organization. Now, they have started exploring more about the cognitive services of Microsoft Azure and trying to utilize the same in their daily routine. It was my privilege and honor, when I got to know that many have completed the Microsoft Build challenge and looking forward for the free certification offered by Microsoft. Okay, what makes so exciting to learn about the Microsoft AI? 1. Microsoft AI are readily available services, which are easy to learn and use. 2. It covers the basic foundation of AI and AI related applications. 3. Demos that are available makes the learning much more interesting. Any learning which allows the learners to dip their hand immediately will always be awesome. Many thanks to Microsoft for making the learning experience awesome. Regards PK
Not getting file path and title in citations in Azure Open AI service
We have developed chat bot for custom data using Azure Open AI service. We are using SharePoint Document library as the data source. We configured data source, index and indexers as mentioned in Microsoft guideline article. We are getting correct answers when we ask the questions, it is giving answer from the files SharePoint document library which we selected as data source. But, issue is, we are not getting file path and title in the citations of the result. We are getting null in id, title, filepath and url. Below it the sample output of our question, I got it from postman: { "id": "*****", "model": "gpt-35-turbo-16k", "created": 1702038809, "object": "extensions.chat.completion", "choices": [ { "index": 0, "finish_reason": "stop", "message": { "role": "assistant", "content": "{ANSWER}", "end_turn": true, "context": { "messages": [ { "role": "tool", "content": "{\"citations\": [{\"content\": \{CONTENT}, \"id\": null, \"title\": null, \"filepath\": null, \"url\": null, \"metadata\": {\"chunking\": \"orignal document size=30129. Scores=2.1690063Org Highlight count=240.Filtering to chunk no. 3/Highlights=42 of size=1605\"}, \"chunk_id\": \"3\"}], \"intent\": \"[\\\"{QUESTION}\\\"]\"}", "end_turn": false } ] } } } ], "usage": { "prompt_tokens": ****, "completion_tokens": ***, "total_tokens": *** } } Can someone please help with this and provide solution for how to get file path and title of the document in citations. Thank You!
