Machine Learning in the Enterprise
Earned juin 10, 2024 EDT
Create ML Models with BigQuery ML
Earned juin 2, 2024 EDT
Feature Engineering
Earned juin 2, 2024 EDT
Launching into Machine Learning
Earned mai 23, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 14, 2024 EDT
Prompt Design in Vertex AI
Earned mai 13, 2024 EDT
Level 3: GenAIus Registries
Earned mai 7, 2024 EDT
Introduction to Large Language Models
Earned mai 7, 2024 EDT
Introduction to Generative AI
Earned mai 7, 2024 EDT
This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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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 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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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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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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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!
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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.
Machine Learning in the Enterprise
Earned juin 10, 2024 EDT
Create ML Models with BigQuery ML
Earned juin 2, 2024 EDT
Feature Engineering
Earned juin 2, 2024 EDT
Launching into Machine Learning
Earned mai 23, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned mai 14, 2024 EDT
Prompt Design in Vertex AI
Earned mai 13, 2024 EDT
Level 3: GenAIus Registries
Earned mai 7, 2024 EDT
Introduction to Large Language Models
Earned mai 7, 2024 EDT
Introduction to Generative AI
Earned mai 7, 2024 EDT