ANASS OULED BEN TAHAR
Member since 2020
Silver League
3520 points
Intro to ML: Image Processing
Earned Sep 7, 2021 EDT
DevOps 必備知識
Earned Nov 27, 2020 EST
Deployment Manager
Earned Nov 27, 2020 EST
安全性與身分識別基礎知識
Earned Nov 24, 2020 EST
雲端工程
Earned Nov 20, 2020 EST
基本概念：基礎架構
Earned Nov 20, 2020 EST
Machine Learning APIs
Earned Oct 26, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Oct 25, 2020 EDT
機器學習簡介：語言處理
Earned Oct 21, 2020 EDT
使用 BigQuery 進行機器學習
Earned Oct 20, 2020 EDT
Data Science on Google Cloud
Earned Sep 22, 2020 EDT
[DEPRECATED] Data Engineering
Earned Sep 22, 2020 EDT
Scientific Data Processing
Earned Sep 19, 2020 EDT
DEPRECATED BigQuery for Data Analysis
Earned Sep 19, 2020 EDT
基本概念：資料、機器學習和 AI
Earned Sep 17, 2020 EDT
DEPRECATED Exploring APIs
Earned Apr 4, 2020 EDT
Cloud Development
Earned Apr 3, 2020 EDT
Google Workspace 必備知識
Earned Apr 3, 2020 EDT
[DEPRECATED] Deploying Applications
Earned Mar 30, 2020 EDT
DEPRECATED Websites and Web Applications
Earned Mar 30, 2020 EDT
Baseline: Deploy & Develop
Earned Mar 29, 2020 EDT
Google Cloud 必備知識
Earned Mar 28, 2020 EDT
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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透過 DevOps 取得 競爭優勢。DevOps 作業涵蓋組織與文化層面，目標為加速推送軟體、 提高服務穩定性，並為所有軟體相關人員 建立共同擁有權。這堂課程說明如何使用 Google Cloud 提高軟體推送功能的速度、穩定性、可用性和安全性。 開發運作研究與評估計畫已新增至 Google Cloud。想知道自己的團隊表現如何嗎？完成 這個五道選擇題的測驗就能知道答案！
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If you’re looking to take your Google Cloud application to the next level, look no further than Deployment Manager. By automating the creation of GCP resources and services, Deployment Manager lets you focus on developing rather than maintaining. In this advanced-level quest, you will get hands on practice with Deployment Manager by building custom templates, automating Python and Jinja application instances, and scaling custom networks.
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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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本入門課程有別於其他課程。 透過這些實驗室，IT 專業人員將有機會實際練習， 熟悉出現在 Google Cloud 助理雲端工程師認證中的主題和服務。本課程包含多個專門的實驗室，從 IAM、網路建立 到 Kubernetes Engine 部署作業， 可全面驗收您的 Google Cloud 知識。請注意，雖然進行這些 實驗室可提升您的技能和能力，但仍建議同時詳閱 測驗指南和其他可用的準備資源。
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如果您是剛起步的雲端開發人員， 想在 Google Cloud Essentials 外獲得更多實作經驗，歡迎參加本課程。您將透過實作實驗室， 深入瞭解 Cloud Storage 和其他重要應用程式服務，例如： Monitoring 和 Cloud Functions。您將習得 在任何 Google Cloud 專案都適用的寶貴技能。
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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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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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。
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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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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
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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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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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Workspace 是 Google 的協作應用程式 平台，由 Google Cloud 提供。在這堂入門課程中，您將 從使用者角度實作練習 Workspace 的核心應用程式。雖然 Workspace 包含的應用程式和工具元件遠比本課程介紹的還要多， 您將體驗的是主要應用程式，包括 Gmail、日曆、試算表以及 其他幾項。每個實驗室功能需要 10 至 15 分鐘來完成，但您也可以視需要花更多時間 自行探索應用程式。
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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.
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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
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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。
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