VENU P
Учасник із 2023
Діамантова ліга
Кількість балів: 39085
Encoder-Decoder Architecture
Earned вер. 19, 2024 EDT
Attention Mechanism
Earned вер. 19, 2024 EDT
[DEPRECATED] Gemini for end-to-end SDLC
Earned вер. 19, 2024 EDT
[DEPRECATED] Gemini for Security Engineers
Earned вер. 19, 2024 EDT
[DEPRECATED] Gemini for Cloud Architects
Earned вер. 19, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud - Yкраїнська
Earned вер. 19, 2024 EDT
Introduction to Responsible AI - Українська
Earned вер. 19, 2024 EDT
Introduction to Large Language Models - Українська
Earned вер. 19, 2024 EDT
Introduction to Image Generation
Earned вер. 19, 2024 EDT
Introduction to Generative AI - Українська
Earned вер. 19, 2024 EDT
Prepare Data for Looker Dashboards and Reports
Earned бер. 29, 2024 EDT
Analyzing and Visualizing Data in Looker
Earned бер. 29, 2024 EDT
Applying Advanced LookML Concepts in Looker
Earned бер. 28, 2024 EDT
Create ML Models with BigQuery ML
Earned бер. 28, 2024 EDT
Developing Data Models with LookML
Earned бер. 25, 2024 EDT
Data Catalog Fundamentals
Earned бер. 16, 2024 EDT
Derive Insights from BigQuery Data
Earned бер. 16, 2024 EDT
Build LookML Objects in Looker
Earned бер. 14, 2024 EDT
Manage Data Models in Looker
Earned бер. 13, 2024 EDT
Google Cloud Data Analytics Essentials
Earned бер. 11, 2024 EDT
Google Cloud Computing Foundation with Kubernetes
Earned лют. 5, 2024 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned груд. 31, 2023 EST
Google Cloud Computing Foundations: Networking and Security in Google Cloud
Earned лист. 27, 2023 EST
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Earned лист. 27, 2023 EST
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Earned лист. 27, 2023 EST
Початок роботи з даними, машинним навчанням і штучним інтелектом
Earned лист. 26, 2023 EST
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned лист. 26, 2023 EST
Початок роботи з інфраструктурою
Earned лист. 25, 2023 EST
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud
Earned жовт. 10, 2023 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Earned жовт. 10, 2023 EDT
Налаштування середовища для розробки додатка в Google Cloud
Earned жовт. 10, 2023 EDT
Google Cloud Computing Foundations: Networking & Security in Google Cloud
Earned жовт. 9, 2023 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned жовт. 9, 2023 EDT
Build a Secure Google Cloud Network
Earned жовт. 9, 2023 EDT
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Earned жовт. 9, 2023 EDT
Налаштування Cloud Load Balancing для Compute Engine
Earned жовт. 9, 2023 EDT
Основи Google Cloud
Earned жовт. 9, 2023 EDT
This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you use Google products and services to develop, test, deploy, and manage applications. With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data. Using a hands-on lab, you experience how Gemini improves the software development lifecycle (SDLC).
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps you secure your cloud environment and resources. You learn how to deploy example workloads into an environment in Google Cloud, identify security misconfigurations with Gemini, and remediate security misconfigurations with Gemini. Using a hands-on lab, you experience how Gemini improves your cloud security posture.
Duet AI was renamed to Gemini, our next-generation model.
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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow.
Duet AI was renamed to Gemini, our next-generation model.
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Що більше штучний інтелект і машинне навчання використовуються в корпоративних середовищах, то нагальнішою стає потреба розробити принципи відповідального ставлення до них. Однак говорити про принципи відповідального використання штучного інтелекту легше, ніж застосовувати їх на практиці. Цей курс допоможе вам дізнатись, як запровадити відповідальну роботу зі штучним інтелектом у вашій організації.
У цьому курсі ви дізнаєтеся про підхід Google Cloud до відповідального використання ШІ, а також отримаєте практичні поради й набудете досвіду, який допоможе вам розробити власний підхід до цього завдання.
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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.
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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Gemini Enterprise Agent Platform.
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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.
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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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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.
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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.
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Complete the intermediate Manage Data Models in Looker skill badge course to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies.
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Discover how to turn raw numbers into clear, data-driven decisions on Google Cloud. Through hands-on labs and practical real-world scenarios, this beginner-level course guides you through querying data in BigQuery, exploring trends in Looker, and crafting shareable, interactive reports in Data Studio.
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This module helps you build a robust understanding of concepts related to Networking, Security & Kubernetes Engine.
This course is part of a series of courses called Google Cloud Computing Foundations.
The courses should be completed in the following order:
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Google Cloud Computing Foundations: Networking and Security in Google Cloud
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Google Cloud Computing Foundation with Kubernetes
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The Google Cloud Computing Foundations courses are for
individuals with little to no background or experience in cloud computing.
They provide a detailed overview of concepts covering cloud basics, big data,
and machine learning, and where and how Google Cloud fits in.
By the end of the course, learners will be able to articulate concepts
around cloud computing, big data, and machine learning, and demonstrate some
hands-on skills.
This course is part of a series of courses called Google Cloud Computing Foundations.
The courses should be completed in the following order:
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Google Cloud Computing Foundations: Networking and Security in Google Cloud
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Google Cloud Computing Foundation with Kubernetes
This final course in the series rev…
Докладніше
The Google Cloud Computing Foundations courses are for
individuals with little to no background or experience in cloud computing.
They provide a detailed overview of concepts covering cloud basics, big data,
and machine learning, and where and how Google Cloud fits in.
By the end of the course, learners will be able to articulate concepts
around cloud computing, big data, and machine learning, and demonstrate some
hands-on skills.
This course is part of a series of courses called Google Cloud Computing Foundations.
The courses should be completed in the following order:
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Google Cloud Computing Foundations: Networking and Security in Google Cloud
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Google Cloud Computing Foundation with Kubernetes
This third course covers how to build secure networks, …
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Докладніше
The Google Cloud Computing Foundations courses are for individuals with little to no
background or experience in cloud computing. They provide an overview of concepts central
to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts
and demonstrate some hands-on skills.
This course is part of a series of courses called Google Cloud Computing Foundations.
The courses should be completed in the following order:
Google Cloud Computing Foundations: Cloud Computing Fundamentals
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Google Cloud Computing Foundations: Networking and Security in Google Cloud
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Google Cloud Computing Foundations: Kubernetes Engine Fundamentals
This first course provides an overview of cloud computing, ways to use Google Cloud, …
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Зараз усі говорять про масиви даних, машинне навчання й штучний інтелект, але це досить вузькоспеціалізовані теми, про які важко знайти матеріали, зрозумілі не лише спеціалістам. На щастя, Google Cloud пропонує зручні сервіси в цих галузях, а завдяки цьому вступному курсу ви зможете ознайомитися з такими інструментами, як BigQuery, Cloud Speech API і Video Intelligence.
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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.
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Якщо ви лише пробуєте розробляти хмарні рішення й шукаєте практичні заняття на додаток до кваліфікаційного курсу "Знайомство з Google Cloud", тоді цей курс саме для вас. Ви отримаєте прикладний досвід завдяки практичним заняттям, присвяченим Cloud Storage і іншим ключовим сервісам додатків, як-от Monitoring і Cloud Functions. Ви отримаєте цінні навички, які можна застосовувати в будь-яких проєктах Google Cloud.
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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This third course covers cloud automation and management tools and building secure networks.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
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Earn a skill badge by completing the Build a Secure Google Cloud Network skill badge course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This first course provides an overview of cloud computing, ways to use Google Cloud, and different compute options.
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Пройдіть вступний кваліфікаційний курс Налаштування Cloud Load Balancing для Compute Engine, щоб продемонструвати свої навички: створення й розгортання віртуальних машин у Compute Engine; налаштування мережі й розподілювачів навантаження додатків.
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На цьому вступному курсі ви отримаєте практичний досвід роботи з основними інструментами й сервісами Google Cloud. Також вам доступні додаткові відео, які допоможуть краще зрозуміти контекст і закріпити знання щодо тем, розглянутих у практичних заняттях. Google Cloud "Основи" – це рекомендований початковий курс для тих, хто починає вивчати Google Cloud. Ви можете розпочати навчання, маючи мінімальні знання про хмарні технології або взагалі не маючи їх, і отримати практичний досвід, який зможете застосувати у своєму першому хмарному проєкті Google Cloud. "Основи Google Cloud" дають вичерпне уявленні про ключові функції платформи – від написання команд у Cloud Shell і розгортання першої віртуальної машини до запуску додатків на Kubernetes Engine і використання розподілу навантаження.
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