Emmanuel Kabu
Member since 2024
Diamond League
12050 points
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Mar 27, 2026 EDT
Google DeepMind: 02 Represent Your Language Data
Earned Oca 19, 2026 EST
Google DeepMind: 01 Build Your Own Small Language Model
Earned Oca 18, 2026 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned Oca 17, 2026 EST
Machine Learning Operations (MLOps) with Agent Platform: Model Evaluation
Earned Oca 15, 2026 EST
MLOps with Agent Platform: Manage Features
Earned Oca 15, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned Oca 14, 2026 EST
[DEPRECATED] Feature Engineering
Earned Oca 9, 2026 EST
Build a Certification Study Guide: PMLE
Earned Oca 8, 2026 EST
Üretken Yapay Zeka İçin Makine Öğrenimi Operasyonları (MLOps)
Earned Oca 8, 2026 EST
Create Generative AI Apps on Google Cloud
Earned Oca 8, 2026 EST
Geliştiriciler İçin Sorumlu Yapay Zeka: Gizlilik ve Güvenlik
Earned Oca 7, 2026 EST
Geliştiriciler için Sorumlu Yapay Zeka: Yorumlanabilirlik ve Şeffaflık
Earned Oca 7, 2026 EST
Create ML Models with BigQuery ML
Earned Oca 7, 2026 EST
Geliştiriciler İçin Sorumlu Yapay Zeka: Adalet ve Önyargı
Earned Ara 30, 2025 EST
Working with Notebooks in Vertex AI
Earned Ara 30, 2025 EST
Introduction to AI and Machine Learning on Google Cloud
Earned Ara 27, 2025 EST
[DEPRECATED] Sorumlu Yapay Zekaya Giriş
Earned Ara 20, 2025 EST
Büyük Dil Modellerine Giriş
Earned Ara 20, 2025 EST
Üretken Yapay Zekaya Giriş
Earned Ara 20, 2025 EST
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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In this Google DeepMind course you will learn how to prepare text data for language models to process. You will investigate the tools and techniques used to prepare, structure, and represent text data for language models, with a focus on tokenization and embeddings. You will be encouraged to think critically about the decisions behind data preparation, and what biases within the data may be introduced into models. You will analyze trade-offs, learn how to work with vectors and matrices, how meaning is represented in language models. Finally, you will practice designing a dataset ethically using the Data Cards process, ensuring transparency, accountability, and respect for community values in AI development.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.
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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.
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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Agent Platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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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.
Learners will get hands-on practice using Agent Platform Feature Store's streaming ingestion at the SDK layer.
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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 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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.
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Bu kurs, MLOps ekiplerinin üretken yapay zeka modellerini dağıtırken ve yönetirken karşılaştığı zorlukların üstesinden gelmek için gereken bilgi ve araçları sağlamaktadır. Ayrıca yapay zeka ekiplerinin, MLOps süreçlerini kolaylaştırıp üretken yapay zeka projelerinde başarıya ulaşması için Vertex AI'ın nasıl yardımcı olduğunu öğrenmenizi amaçlamaktadır.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application. Explore other content in the Gemini Agent Ready (GEAR) program.
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Bu kursta, yapay zekada gizlilik ve güvenlik konuları ele alınmaktadır. Kurs boyunca, Google Cloud ürünleri ve açık kaynak araçları kullanarak yapay zekayla ilgili önerilen gizlilik ve güvenlik uygulamalarını benimsemenize yardımcı olacak pratik yöntemler ile araçları tanıyacaksınız.
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Bu kursta yapay zekanın yorumlanabilirliği ve şeffaflığı kavramlarıyla ilgili temel bilgiler sunulmaktadır. Ayrıca geliştiriciler ve mühendisler için yapay zeka sistemlerinde şeffaflığın önemi ele alınmaktadır. Kurs boyunca, veri ve yapay zeka modellerinde yorumlanabilirliğin ve şeffaflığın sağlanmasına yardımcı olacak pratik yöntemleri ve araçları tanıyacaksınız.
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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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Bu kursta, sorumlu yapay zeka kavramı ve yapay zeka ilkeleri tanıtılmaktadır. Kurs, adalet ve önyargıyı pratik şekilde tanımlama teknikleri ile yapay zeka/makine öğrenimi uygulamalarında önyargının azaltılması konularını ele almaktadır. Kurs boyunca, Google Cloud ürünleri ve açık kaynaklı araçları kullanarak sorumlu yapay zekayla ilgili en iyi uygulamaları benimsemenize yardımcı olacak pratik yöntemler ve araçları tanıyacaksınız.
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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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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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Giriş seviyesindeki bu mikro öğrenme kursu, sorumlu yapay zekanın ne olduğunu, neden önemli olduğunu ve Google'ın sorumlu yapay zekayı ürünlerinde nasıl uyguladığını açıklamayı amaçlamaktadır. Ayrıca Google'ın 3 yapay zeka ilkesini de tanıtır.
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Giriş seviyesindeki bu mikro öğrenme kursunda; büyük dil modellerinin (LLM) ne olduğu, hangi kullanım alanlarında kullanılabilecekleri ve istem ayarlama ile LLM performansını nasıl artırabileceğiniz ele alınmaktadır. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirebileceğiniz Google araçları da yer almaktadır.
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Bu giriş seviyesi mikro öğrenme kursu, üretken yapay zekanın ne olduğunu, nasıl kullanıldığını ve geleneksel makine öğrenimi yöntemlerinden farkını açıklamayı amaçlamaktadır. Ayrıca kendi üretken yapay zeka uygulamalarınızı geliştirebileceğiniz Google araçları da ele alınmaktadır.
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