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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. Complete this activity and earn a badge! Boost your cloud career by showing the world the skills you’ve developed. This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Explore the world of Artificial Intelligence (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!" In this curriculum, you will learn: - Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI). - Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch. - Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art. - Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems. What we will not cover in this curriculum: - Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD. - Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum. - Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others. - Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths. - Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail. - Deep Mathematics behind deep learning. For this, we would recommend Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at 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 Learning Path. Go to github course page

What you'll learn: ✔️Describe what is AI, its applications, use cases, and how it is transforming our lives ✔️Explain terms like Machine Learning, Deep Learning and Neural Networks ✔️Describe several issues and ethical concerns surrounding AI ✔️Articulate advice from experts about learning and starting a career in AI Build your subject-matter expertise This course is available as part of multiple programs When you enroll in this course, you'll also be asked to select a specific program. ✔️Learn new concepts from industry experts ✔️Gain a foundational understanding of a subject or tool ✔️Develop job-relevant skills with hands-on projects ✔️Earn a shareable career certificate

This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles. Complete this activity and earn a badge! Boost your cloud career by showing the world the skills you’ve developed.

This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. Complete this activity and earn a badge! Boost your cloud career by showing the world the skills you’ve developed.

What you'll learn: ✔️Learn the skills needed to be successful in a machine learning engineering role ✔️Prepare for the Google Cloud Professional Machine Learning Engineer certification exam ✔️Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies ✔️Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications Advance your career with in-demand skills: ✔️Receive professional-level training from Google Cloud ✔️Demonstrate your technical proficiency ✔️Earn an employer-recognized certificate from Google Cloud ✔️Prepare for an industry certification exam

There are 4 modules in this course AI isn't just for engineers. If you want your organization to become better at using AI, this is a course you should encourage everyone to take—especially your non-technical colleagues. In this course, you will learn: - The meaning of common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI can—and cannot— actually do - How to find opportunities to apply AI to solve problems in your business organizations - What it's like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate the ethical and public debate around AI Although this course is primarily non-technical, engineers can also take this course to learn about the business aspects of AI.

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well! Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'. ✍️ Hearty thanks to our authors Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd 🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper 🙏 Special thanks 🙏 to our Microsoft Student Ambassador authors, reviewers, and content contributors, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal 🤩 Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!