Mettez en pratique vos compétences dans la console Google Cloud
Dhruv Saija
Date d'abonnement : 2025
Ligue d'Or
9390 points
Introduction to Responsible AI
Earned mai 9, 2025 EDT
Introduction to Large Language Models
Earned mai 9, 2025 EDT
Introduction to Generative AI
Earned mai 9, 2025 EDT
Create ML Models with BigQuery ML
Earned mai 9, 2025 EDT
Working with Notebooks in Vertex AI
Earned mai 8, 2025 EDT
Professional Machine Learning Engineer Study Guide
Earned mai 7, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned mai 6, 2025 EDT
Build Real World AI Applications with Gemini and Imagen
Earned avr. 30, 2025 EDT
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.
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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 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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This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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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 Vertex AI platform.
Introduction to Responsible AI
Earned mai 9, 2025 EDT
Introduction to Large Language Models
Earned mai 9, 2025 EDT
Introduction to Generative AI
Earned mai 9, 2025 EDT
Create ML Models with BigQuery ML
Earned mai 9, 2025 EDT
Working with Notebooks in Vertex AI
Earned mai 8, 2025 EDT
Professional Machine Learning Engineer Study Guide
Earned mai 7, 2025 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned mai 6, 2025 EDT
Build Real World AI Applications with Gemini and Imagen
Earned avr. 30, 2025 EDT