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Marcos Dal Maso

Mitglied seit 2023

Bronze League

400 Punkte
Badge für Generative AI Fundamentals Generative AI Fundamentals Earned Sep 21, 2023 EDT
Badge für Introduction to Responsible AI - Deutsch Introduction to Responsible AI - Deutsch Earned Jul 9, 2023 EDT
Badge für Feature Engineering Feature Engineering Earned Jul 9, 2023 EDT
Badge für TensorFlow on Google Cloud TensorFlow on Google Cloud Earned Jul 7, 2023 EDT
Badge für Launching into Machine Learning Launching into Machine Learning Earned Jul 6, 2023 EDT
Badge für How Google Does Machine Learning How Google Does Machine Learning Earned Jul 5, 2023 EDT
Badge für Google Cloud Big Data and Machine Learning Fundamentals Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 22, 2023 EDT
Badge für Introduction to Large Language Models - Deutsch Introduction to Large Language Models - Deutsch Earned Jun 22, 2023 EDT
Badge für Introduction to Generative AI - Deutsch Introduction to Generative AI - Deutsch Earned Jun 22, 2023 EDT

Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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In diesem Einführungskurs im Microlearning-Format wird erklärt, was verantwortungsbewusste Anwendung von KI bedeutet, warum sie wichtig ist und wie Google dies in seinen Produkten berücksichtigt. Darüber hinaus werden die 7 KI-Grundsätze von Google behandelt.

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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 designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

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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 explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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In diesem Einführungskurs im Microlearning-Format wird untersucht, was Large Language Models (LLM) sind, für welche Anwendungsfälle sie genutzt werden können und wie die LLM-Leistung durch Feinabstimmung von Prompts gesteigert werden kann. Darüber hinaus werden Tools von Google behandelt, die das Entwickeln eigener Anwendungen basierend auf generativer KI ermöglichen.

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In diesem Einführungskurs im Microlearning-Format wird erklärt, was generative KI ist, wie sie genutzt wird und wie sie sich von herkömmlichen Methoden für Machine Learning unterscheidet. Darüber hinaus werden Tools von Google behandelt, mit denen Sie eigene Anwendungen basierend auf generativer KI entwickeln können.

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