Implement Multimodal Vector Search with BigQuery
Earned mai 13, 2026 EDT
Engineer AI Agents with Agent Development Kit (ADK)
Earned mai 13, 2026 EDT
Deploy Multi-Agent Architectures
Earned mai 12, 2026 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned mai 12, 2026 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 11, 2026 EDT
Explore Generative AI in Agent Platform
Earned mai 11, 2026 EDT
Develop AI-Powered Prototypes in Google AI Studio
Earned mai 6, 2026 EDT
Build Real World AI Applications with Gemini and Imagen
Earned mai 6, 2026 EDT
Prompt Design in Agent Platform
Earned mai 5, 2026 EDT
Introduction to Responsible AI
Earned avr. 22, 2026 EDT
Introduction to Large Language Models
Earned avr. 22, 2026 EDT
Introduction to Generative AI
Earned avr. 22, 2026 EDT
Complete the intermediate Implement Multimodal Vector Search with BigQuery skill badge to demonstrate skills in the following: using Gemini in BigQuery to generate and debug SQL, conduct sentiment analysis, summarize text and identify keywords, generate embeddings, create a Retrieval Augmented Generation (RAG) pipeline, and implement multimodal vector search.
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Complete the intermediate Engineer AI Agents with Agent Development Kit (ADK) skill badge by completing this course to demonstrate skills in the following: formulating real-world language model research problems; building a simple tokenizer; preparing a dataset for training a transformer language model; running the training loop of a small language model.
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Complete the advanced Deploy Multi-Agent Architectures skill badge to demonstrate skills in the following: building multi-agent systems with ADK, connecting agents with the Agent-to-Agent (A2A) protocol, integrating external tools using the Model Context Protocol (MCP), and deploying a complete multi-agent solution to Agent Engine.
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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini.
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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run.
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Complete the intermediate Explore Generative AI in Agent Platform skill badge to demonstrate skills in text generation, image and video analysis for enhanced content creation, and applying function calling techniques within the Gemini API. Discover how to leverage sophisticated Gemini techniques, explore multimodal content generation, and expand the capabilities of your AI-powered projects.
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Complete the introductory Develop AI-Powered Prototypes in Google AI Studio skill badge to demonstrate skills in the following: crafting effective prompts, leveraging multimodal capabilities for image and video analysis, prototyping functional AI-driven applications from templates and text prompts, and utilizing API keys to build and deploy custom AI solutions.
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Complete the introductory Build Real World AI Applications with Gemini and Imagen skill badge to demonstrate skills in the following: image recognition, natural language processing, image generation using Google's powerful Gemini and Imagen models, deploying applications on the Google Cloud Agent Platform.
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Complete the introductory Prompt Design in Agent Platform skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Agent Platform. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
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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 3 AI principles.
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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.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
Implement Multimodal Vector Search with BigQuery
Earned mai 13, 2026 EDT
Engineer AI Agents with Agent Development Kit (ADK)
Earned mai 13, 2026 EDT
Deploy Multi-Agent Architectures
Earned mai 12, 2026 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned mai 12, 2026 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 11, 2026 EDT
Explore Generative AI in Agent Platform
Earned mai 11, 2026 EDT
Develop AI-Powered Prototypes in Google AI Studio
Earned mai 6, 2026 EDT
Build Real World AI Applications with Gemini and Imagen
Earned mai 6, 2026 EDT
Prompt Design in Agent Platform
Earned mai 5, 2026 EDT
Introduction to Responsible AI
Earned avr. 22, 2026 EDT
Introduction to Large Language Models
Earned avr. 22, 2026 EDT
Introduction to Generative AI
Earned avr. 22, 2026 EDT