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Mettez en pratique vos compétences dans la console Google Cloud

Vithal Madhira

Date d'abonnement : 2017

Ligue d'Argent

3515 points
Google Cloud: Prompt Engineering Guide Earned fév. 26, 2025 EST
Preparing for your Professional Data Engineer Journey Earned juil. 16, 2024 EDT
Introduction to Large Language Models Earned déc. 30, 2023 EST
Introduction to Generative AI Earned déc. 30, 2023 EST
Feature Engineering Earned avr. 14, 2022 EDT
How Google Does Machine Learning Earned avr. 6, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned avr. 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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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.

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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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