Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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大數據、機器學習和人工智慧 (AI) 是時下熱門的 電腦相關話題，但這些領域相當專業，就算想要入門 也難以取得教材或資料。幸好，Google Cloud 提供了此領域的多種服務，而且容易使用。 參加這堂入門課程，您就能踏出第一步， 開始學習運用 BigQuery、Cloud Speech API 以及 Video Intelligence 等工具。
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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大家都知道，機器學習是發展最快的科技領域之一， 而 Google Cloud Platform 在這方面功不可沒。 GCP 提供多種 API，凡是與機器學習相關的任務，幾乎都能處理。您將在本入門課程的 實驗室，實際演練機器學習技術 在語言處理方面的應用，學會如何從文中擷取實體資訊、 執行情緒和語法分析，並使用 Speech-to-Text API 轉錄語音。
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本課程最適合從事科技或金融職務， 且負責管理 Google Cloud 費用的人士修習。您將學習如何設定帳單帳戶、 整理資源及管理帳單存取權限。 在實作實驗室，您會瞭解如何查看帳單、從帳單報表追蹤 Google Cloud 費用、 使用 BigQuery 或 Google 試算表分析帳單資料， 以及使用 Looker Studio 建立自訂的帳單資訊主頁。如需影片提及的參考資源連結， 請參閱其他資源文件。
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Google Cloud 的服務在安全上絕不妥協， 因此開發了專用工具，確保所有專案安全無虞， 使用者也能妥善管理身分識別機制。在這堂入門課程中，您會實際使用 Google Cloud 的 Identity and Access Management (IAM) 服務， 練習管理使用者和虛擬機器帳戶。您將 佈建虛擬私有雲和 VPN 來熟悉網路安全功能，並瞭解有哪些工具 可防範資安威脅和資料遺失。
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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在這堂入門課程，您將實際練習使用 Google Cloud 的基礎工具和服務。本課程包含可選擇觀賞的影片， 針對實驗室涵蓋的概念提供更多背景資訊，協助您複習。「Google Cloud 必備知識」 是適合 Google Cloud 學員的第一堂課， 即使您尚未學習或不熟悉雲端知識， 也能從這堂課獲得實務經驗，並應用於第一項 Google Cloud 專案。不管是撰寫 Cloud Shell 指令 和部署第一部虛擬機器，還是在 Kubernetes Engine 或透過負載平衡執行應用程式， 「Google Cloud 必備知識」都是認識平台基本功能的最佳入門資源。
在 Google Cloud 為機器學習 API 準備資料
Earned Nov 7, 2021 EST
從 BigQuery 資料取得深入分析結果
Earned Nov 7, 2021 EST
DEPRECATED BigQuery Basics for Data Analysts
Earned Nov 7, 2021 EST
Data Science on Google Cloud
Earned Nov 7, 2021 EST
NCAA® March Madness®: Bracketology with Google Cloud
Earned Nov 1, 2021 EDT
基本概念：資料、機器學習和 AI
Earned Oct 31, 2021 EDT
Intro to ML: Image Processing
Earned Oct 30, 2021 EDT
機器學習簡介：語言處理
Earned Oct 29, 2021 EDT
瞭解 Google Cloud 費用
Earned May 3, 2020 EDT
安全性與身分識別基礎知識
Earned May 3, 2020 EDT
Cloud SQL
Earned Apr 29, 2020 EDT
Google Cloud 必備知識
Earned Apr 22, 2020 EDT