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Pranav Venkatesh

成为会员时间：2023

青铜联赛

21615 积分
Introduction to Generative AI - 简体中文徽章 Introduction to Generative AI - 简体中文 Earned Nov 2, 2023 EDT
Feature Engineering徽章 Feature Engineering Earned Nov 2, 2023 EDT
TensorFlow on Google Cloud徽章 TensorFlow on Google Cloud Earned Nov 2, 2023 EDT
Launching into Machine Learning徽章 Launching into Machine Learning Earned Nov 2, 2023 EDT
Introduction to AI and Machine Learning on Google Cloud - 简体中文徽章 Introduction to AI and Machine Learning on Google Cloud - 简体中文 Earned Nov 2, 2023 EDT
Prepare Data for ML APIs on Google Cloud徽章 Prepare Data for ML APIs on Google Cloud Earned Nov 2, 2023 EDT
Build a Secure Google Cloud Network徽章 Build a Secure Google Cloud Network Earned Nov 2, 2023 EDT
基准：基础架构徽章 基准：基础架构 Earned Nov 2, 2023 EDT
Implement Load Balancing on Compute Engine徽章 Implement Load Balancing on Compute Engine Earned Nov 1, 2023 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud徽章 Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud Earned Nov 1, 2023 EDT
Google Cloud Computing Foundations: Networking & Security in Google Cloud徽章 Google Cloud Computing Foundations: Networking & Security in Google Cloud Earned Oct 30, 2023 EDT
Google Cloud Computing Foundations: Infrastructure in Google Cloud徽章 Google Cloud Computing Foundations: Infrastructure in Google Cloud Earned Oct 30, 2023 EDT
Google Cloud Computing Foundations: Cloud Computing Fundamentals徽章 Google Cloud Computing Foundations: Cloud Computing Fundamentals Earned Oct 29, 2023 EDT

这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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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 covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML 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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本课程介绍 Google Cloud 中的人工智能 (AI) 和机器学习 (ML) 服务，这些服务支持数据到 AI 的生命周期（从 AI 基础、AI 开发到 AI 解决方案）。我们将探索一系列技术、产品和工具；利用这些工具，可基于不同用户（包括数据科学家、AI 开发者和机器学习工程师）的目标构建机器学习模型、机器学习流水线和生成式 AI 项目。

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API. 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 this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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Earn a skill badge by completing the Build and Secure Networks in Google Cloud course, where you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud. 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, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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如果您是新手云开发人员，希望在GCP Essentials之外寻求动手实践，那么此任务适合您。通过深入研究Cloud Storage和其他关键应用程序服务（如Stackdriver和Cloud Functions）的实验室，您将获得实践经验。通过执行此任务，您将开发适用于任何GCP计划的宝贵技能。 1分钟的视频向您介绍这些实验室的关键概念。

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完成入门级在 Compute Engine 上实现负载均衡技能徽章课程，展示自己在以下方面的技能： 编写 gcloud 命令和使用 Cloud Shell，在 Compute Engine 中创建和部署虚拟机， 以及配置网络和 HTTP 负载均衡器。 技能徽章是由 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 该课程会检验您在交互式实操环境中运用所学知识的 能力。完成此技能徽章课程和作为最终评估的实验室挑战赛， 即可获得技能徽章，并在您的圈子中秀一秀。

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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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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

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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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