AI-900: Microsoft Azure AI Fundamentals Study Guide
This comprehensive study guide provides a thorough overview of the topics covered in the Microsoft Azure AI Fundamentals (AI-900) exam, including Artificial Intelligence workloads, fundamental principles of machine learning, computer vision and natural language processing workloads. Learn about the exam's intended audience, how to earn the certification, and the skills measured as of April 2022. Discover the important considerations for responsible AI, the capabilities of Azure Machine Learning Studio and more. Get ready to demonstrate your knowledge of AI and ML concepts and related Microsoft Azure services with this helpful study guide.Microsoft Learn AI Skills Challenge
Join Microsoft's AI Skills Challenge 2023 to enhance your technical expertise in Artificial Intelligence. Register now to access exclusive resources, hands-on labs, and interactive learning sessions. Boost your knowledge in generative AI, machine learning, cognitive services, natural language processing, and computer vision to stay ahead in the ever-evolving world of AI.Getting started with using Visual Machine Learning Tools for building your Machine Learning Models
Machine learning is a technique that uses mathematics and statistics to create a model that can predict unknown values. In this session you explore machine learning and learn how to use the automated machine learning capability of Azure Machine Learning to train and deploy a predictive model.
Build your first ML-Model with ML.NET Model Builder
Excited to dive into machine learning in .NET? With the aid of tools like ML.NET Model Builder and Visual Studio, it's a breeze. Here's a preview of the steps you'll take: 1. Download Visual Studio 2022 with .NET desktop development and ML.NET Model Builder. 2. Create a .NET console app named myMLApp. 3. Add a machine learning model named SentimentModel.mbconfig. 4. Choose the Data classification scenario. 5. Select Local (CPU) as the training environment. 6. Prepare and import your data. 7. Train the model. 8. Evaluate its performance. 9. Consume the model using provided code. 10. Run and debug to observe the results. Now you're all set to leverage ML.NET's prowess for predictive models in your .NET apps!Machine Learning for Sales Forecasting: A Capstone Project with Columbia University
This past semester we have been collaborating on a machine learning Capstone Project with Columbia University’s Master of Science in Applied Analytics: capstone projects are applied and experimental projects where students take what they have learned throughout the course of their graduate program and apply it to examine a specific area of study.Microsoft Student Summit March 2023 - Start and Accelerate Your Career in Tech
Student Summit - Start and Accelerate Your Career in Tech In partnership with Microsoft Reactor this exciting 90-minute event will help build your confidence and motivation to skill on the Microsoft Cloud, and coach you on the next steps to continue your learning on topics including - Application Development and Developer Tools, Low Code/ No-Code / Fusion Development, and AI, Data and Machine Learning.
