Chander Valasai
Date d'abonnement : 2025
Ligue de Diamant
17738 points
Agent Fundamentals
Earned mars 24, 2026 EDT
Introduction to AI Agents
Earned fév. 19, 2026 EST
Encoder-Decoder Architecture
Earned mars 11, 2025 EDT
Attention Mechanism
Earned mars 11, 2025 EDT
Scaling with Google Cloud Operations
Earned mars 11, 2025 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned mars 11, 2025 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned mars 11, 2025 EDT
Analyze Images with the Cloud Vision API
Earned fév. 20, 2025 EST
Using the Google Cloud Speech API
Earned fév. 19, 2025 EST
Analyze Sentiment with Natural Language API
Earned fév. 19, 2025 EST
Create ML Models with BigQuery ML
Earned fév. 18, 2025 EST
Introduction to AI and Machine Learning on Google Cloud
Earned fév. 13, 2025 EST
Generative AI Explorer - Agent Platform
Earned fév. 11, 2025 EST
Introduction to Large Language Models
Earned fév. 11, 2025 EST
Introduction to Generative AI
Earned fév. 11, 2025 EST
Working with Notebooks in Vertex AI
Earned fév. 10, 2025 EST
Build a Certification Study Guide: PMLE
Earned fév. 10, 2025 EST
This course introduces the fundamentals of AI agents and explores where agents add value in the real world. It provides a foundation for developers, architects, and technical decision-makers who want to understand AI systems through the lens of autonomous, goal-directed behavior.
Explore other content in the Gemini Agent Ready (GEAR) program.
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Gain a conceptual overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems. You’ll explore the technical architecture—models, tools, and orchestration—that enables agents to learn, plan, and achieve goals on your behalf.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
As part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Gemini Enterprise Agent Platform empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.
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Earn a skill badge by completing the Using the Google Cloud Speech API skill badge course, where you learn how create a Speech-to-Text API request, transcribe audio speech to text, and transcribe speech.
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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you learn how the API derives sentiment from text.
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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
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The Generative AI Explorer - Agent Platform course is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the Gemini models in Agent Platform. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via custom training in Agent Platform and deploy it to an endpoint in Agent Platform.
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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. Explore other content in the Gemini Agent Ready (GEAR) program.
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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.
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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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