Natural Language Processing on Google Cloud
Earned Ağu 3, 2024 EDT
How Google Does Machine Learning
Earned Tem 20, 2024 EDT
Learn how to create Hybrid Search applications using Vertex AI Vertex Search to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
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Complete the Edit images with Imagen skill badge to demonstrate your skills with Imagen's mask modes and editing modes to edit images according to certain prompts.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Explore a variety of techniques for using Gemini to understand image, audio, video, and live-streaming media. You will discover how meaningful information can be extracted from each of these forms of media to use in media-rich applications.
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Generate engaging media with Google's foundation models for media. Create new images with Imagen, or edit your existing photos by adding details or outpainting to create a wider view. Replace backgrounds to put your products in new scenes. And learn the basics of generating videos with Veo!
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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.
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Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!"
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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Kurumsal yapay zeka ve makine öğreniminin kullanımı artmaya devam ettikçe, bunu sorumlu bir şekilde oluşturmanın önemi de artıyor. Sorumlu yapay zeka hakkında konuşmanın, onu uygulamaya koymaktan çok daha kolay olabilmesi burada bir zorluk oluşturmaktadır. Kuruluşunuzda sorumlu yapay zekayı nasıl işlevsel hale getireceğinizi öğrenmekle ilgileniyorsanız, bu kurs tam size göre.
Bu kurs, Google Cloud'un sorumlu yapay zeka yaklaşımını nasıl uyguladığını derinlemesine inceleyerek, kendi sorumlu yapay zeka stratejinizi oluşturmanız için size kapsamlı bir çerçeve sunuyor.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning 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 introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Dataproc'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma. Beceri rozeti, Google Cloud ürün ve hizmetlerindeki uzmanlık düzeyiniz karşılığında Google Cloud tarafından verilen özel bir dijital rozettir. Bilgilerinizi, etkileşimli ve uygulamalı bir ortamda kullanma becerinizi test eder. Ağınızla paylaşabileceğiniz bir beceri rozeti kazanmak için bu beceri rozeti kursunu ve son değerlendirme niteliğindeki yarışma laboratuvarını tamamlayın.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI 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.
Implement Hybrid Search
Earned Eyl 15, 2025 EDT
Edit images with Imagen
Earned Eyl 14, 2025 EDT
Understand and Respond to Media
Earned Eyl 14, 2025 EDT
Generate and Edit Media with Imagen, Gemini, and Veo
Earned Eyl 12, 2025 EDT
Empower Gen AI apps with tool use
Earned Eyl 12, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned Eyl 12, 2025 EDT
Explore Google's Gen AI Models
Earned Eyl 10, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery
Earned Eyl 8, 2025 EDT
Engineer Effective Prompts for Generative Models
Earned Eyl 6, 2025 EDT
Sorumlu Yapay Zeka: Google Cloud ile Yapay Zeka İlkelerinin Uygulanması
Earned Ağu 31, 2025 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Tem 15, 2025 EDT
Build a Data Warehouse with BigQuery
Earned Tem 13, 2025 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned Şub 9, 2025 EST
Launching into Machine Learning
Earned Şub 2, 2025 EST
Introduction to AI and Machine Learning on Google Cloud
Earned Oca 27, 2025 EST
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama
Earned Ağu 25, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Ağu 18, 2024 EDT
Natural Language Processing on Google Cloud
Earned Ağu 3, 2024 EDT
How Google Does Machine Learning
Earned Tem 20, 2024 EDT