Manage Data Models in Looker
Earned juil. 25, 2024 EDT
App Building with AppSheet
Earned juil. 24, 2024 EDT
Put It All Together: Prepare for a Cloud Data Analyst Job
Earned mai 26, 2024 EDT
Create Image Captioning Models
Earned mai 26, 2024 EDT
Encoder-Decoder Architecture
Earned mai 26, 2024 EDT
Transformer Models and BERT Model
Earned mai 26, 2024 EDT
Attention Mechanism
Earned mai 22, 2024 EDT
Introduction to Image Generation
Earned mai 20, 2024 EDT
Introduction to Large Language Models
Earned mai 14, 2024 EDT
Feature Engineering
Earned mai 14, 2024 EDT
Introduction to Data Analytics in Google Cloud
Earned mai 7, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 6, 2024 EDT
Prompt Design in Vertex AI
Earned mai 6, 2024 EDT
Introduction to Generative AI
Earned mai 6, 2024 EDT
Level 3: GenAIus Registries
Earned mai 6, 2024 EDT
Complete the intermediate Manage Data Models in Looker skill badge course to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies.
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Earn a skill badge by completing the App Building with AppSheet course, where you learn how to build, configure, and publish apps using AppSheet.
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This is the fifth of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll combine and apply the foundational knowledge and skills from courses 1-4 in a hands-on Capstone project that focuses on the full data lifecycle project. You’ll practice using cloud-based tools to acquire, store, process, analyze, visualize, and communicate data insights effectively. By the end of the course, you’ll have completed a project demonstrating their proficiency in effectively structuring data from multiple sources, presenting solutions to varied stakeholders, and visualizing data insights using cloud-based software. You’ll also update your resume and practice interview techniques to help prepare for applying and interviewing for jobs.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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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 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.
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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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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.
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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 course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This is the first of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll define the field of cloud data analysis and describe roles and responsibilities of a cloud data analyst as they relate to data acquisition, storage, processing, and visualization. You’ll explore the architecture of Google Cloud-based tools, like BigQuery and Cloud Storage, and how they are used to effectively structure, present, and report data.
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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 introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. 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 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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Today, developers need all the tools to shine. Artifact Registry is your one-stop shop for storing and managing your code. Learn how to start building your dream code and earn a Google Cloud Credential along the way!- No prior experience needed!
Manage Data Models in Looker
Earned juil. 25, 2024 EDT
App Building with AppSheet
Earned juil. 24, 2024 EDT
Put It All Together: Prepare for a Cloud Data Analyst Job
Earned mai 26, 2024 EDT
Create Image Captioning Models
Earned mai 26, 2024 EDT
Encoder-Decoder Architecture
Earned mai 26, 2024 EDT
Transformer Models and BERT Model
Earned mai 26, 2024 EDT
Attention Mechanism
Earned mai 22, 2024 EDT
Introduction to Image Generation
Earned mai 20, 2024 EDT
Introduction to Large Language Models
Earned mai 14, 2024 EDT
Feature Engineering
Earned mai 14, 2024 EDT
Introduction to Data Analytics in Google Cloud
Earned mai 7, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 6, 2024 EDT
Prompt Design in Vertex AI
Earned mai 6, 2024 EDT
Introduction to Generative AI
Earned mai 6, 2024 EDT
Level 3: GenAIus Registries
Earned mai 6, 2024 EDT