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Diego Alberto Torres Oropeza

Member since 2024

Gold League

10555 points
Boost Productivity with Gemini in BigQuery Earned سبتمبر 30, 2025 EDT
Introduction to Data Engineering on Google Cloud Earned سبتمبر 18, 2025 EDT
[DEPRECATED] Feature Engineering Earned يونيو 18, 2024 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud Earned يونيو 14, 2024 EDT
Launching into Machine Learning Earned أبريل 6, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud Earned مارس 17, 2024 EDT

This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.

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In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.

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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 course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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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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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.

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