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Ihre Kompetenzen in der Google Cloud Console anwenden

Vithal Madhira

Mitglied seit 2017

Silver League

3515 Punkte
Google Cloud: Prompt Engineering Guide Earned Feb 26, 2025 EST
Preparing for your Professional Data Engineer Journey Earned Jul 16, 2024 EDT
Einführung in Large Language Models Earned Dez 30, 2023 EST
Einführung in generative KI Earned Dez 30, 2023 EST
Feature Engineering Earned Apr 14, 2022 EDT
How Google Does Machine Learning Earned Apr 6, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Apr 2, 2022 EDT

Google Cloud : Prompt Engineering Guide examines generative AI tools, how they work. We'll explore how to combine Google Cloud knowledge with prompt engineering to improve Gemini responses.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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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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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 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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