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Gautam Singh
Member since 2024
Using BigQuery Machine Learning for Inference
Earned Jun 26, 2025 EDT
[DEPRECATED]Introduction to Responsible AI
Earned Jun 26, 2025 EDT
Introduction to Large Language Models
Earned Jun 26, 2025 EDT
Encoder-Decoder Architecture
Earned Jun 20, 2025 EDT
[DEPRECATED] Gemini for Data Scientists and Analysts
Earned May 28, 2025 EDT
Build Event-Driven Applications with Eventarc
Earned May 27, 2025 EDT
Store, Process, and Manage Data on Google Cloud - Command Line
Earned May 27, 2025 EDT
Build Serverless Applications with Cloud Run Functions
Earned May 27, 2025 EDT
Introduction to Generative AI
Earned Jul 14, 2024 EDT
Learn about BigQuery ML for Inference, why Data Analysts should use it, its use cases, and supported ML models. You will also learn how to create and manage these ML models in BigQuery.
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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. Explore other content in the Gemini Agent Ready (GEAR) program.
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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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
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Earn a Skill Badge by completing the Build Event-Driven Applications with Eventarc course. In this course, you use Eventarc to create event triggers for different resources, including Pub/Sub topics and Cloud Storage buckets.
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Cloud Storage, Cloud Functions, and Cloud Pub/Sub are all Google Cloud Platform services that can be used to store, process, and manage data. All three services can be used together to create a variety of data-driven applications. In this skill badge you use Cloud Storage to store images, Cloud Functions to process the images, and Cloud Pub/Sub to send the images to another application.
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Earn a Introductory skill badge by completing the Build Serverless Applications with Cloud Run Functions course, where you learn how to use Cloud Run functions through the Google Cloud console and on the command line.
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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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