Adeeba Nousheen
Date d'abonnement : 2025
Ligue d'Or
3041 points
[DEPRECATED]Introduction to Responsible AI
Earned juin 19, 2026 EDT
Introduction to Large Language Models
Earned juin 19, 2026 EDT
Introduction to Generative AI
Earned juin 19, 2026 EDT
Transformer Models and BERT Model
Earned jan. 29, 2026 EST
Encoder-Decoder Architecture
Earned jan. 29, 2026 EST
Attention Mechanism
Earned jan. 23, 2026 EST
Introduction to Image Generation
Earned jan. 23, 2026 EST
Responsible AI for Developers: Interpretability & Transparency
Earned jan. 23, 2026 EST
Responsible AI for Developers: Fairness & Bias
Earned jan. 13, 2026 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned jan. 5, 2026 EST
Machine Learning Operations (MLOps) for Generative AI
Earned déc. 29, 2025 EST
Build a Certification Study Guide: PMLE
Earned déc. 27, 2025 EST
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.
En savoir plus
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 Enterprise Agent Ready (GEAR) program.
En savoir plus
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.
En savoir plus
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
En savoir plus
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.
En savoir plus
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.
En savoir plus
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 Gemini Enterprise Agent Platform.
En savoir plus
This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
En savoir plus
This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.
En savoir plus
This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
En savoir plus
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.
En savoir plus
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.
En savoir plus
