Mayar Shams
Member since 2020
Silver League
6500 points
Bulut Mühendisliği
Earned Eki 12, 2020 EDT
Building Codeless Pipelines on Cloud Data Fusion
Earned Eki 12, 2020 EDT
Google Developer Essentials
Earned Eki 10, 2020 EDT
Intro to ML: Image Processing
Earned Eki 8, 2020 EDT
Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama
Earned Eki 8, 2020 EDT
Compute Engine İçin Cloud Load Balancing'i Uygulama
Earned Eki 8, 2020 EDT
BigQuery Verilerinden Analiz Elde Etme
Earned Eki 7, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Eki 7, 2020 EDT
Data Catalog Fundamentals
Earned Eki 7, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Eki 7, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned Eki 7, 2020 EDT
Cloud SQL
Earned Eki 7, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Eki 6, 2020 EDT
DEPRECATED Applied Data: Blockchain
Earned Eki 6, 2020 EDT
Machine Learning APIs
Earned Eki 6, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Eki 5, 2020 EDT
BigQuery for Machine Learning
Earned Eki 2, 2020 EDT
Intro to ML: Language Processing
Earned Eki 1, 2020 EDT
Data Science on Google Cloud
Earned Eyl 29, 2020 EDT
[DEPRECATED] Data Engineering
Earned Eyl 28, 2020 EDT
Scientific Data Processing
Earned Eyl 24, 2020 EDT
DEPRECATED BigQuery for Data Analysis
Earned Eyl 24, 2020 EDT
Temel: Veri, Makine Öğrenimi, Yapay Zeka
Earned Eyl 22, 2020 EDT
Google Cloud'un Temel Özellikleri
Earned Eyl 13, 2020 EDT
Google Workspace Essentials
Earned Eyl 3, 2020 EDT
Bu giriş seviyesi kurs, diğer kurs teklifleri arasında benzersiz bir yere sahiptir. Laboratuvarlar, BT profesyonellerine Google Cloud Certified Associate Cloud Engineer sertifikasında geçen konular ve hizmetlerle ilgili uygulamalı alıştırma imkanı tanımak üzere tasarlandı. Bu kurs; IAM, ağ iletişimi ve Kubernetes Engine dağıtımı gibi alanlarda Google Cloud bilginizi test edecek özel laboratuvarlardan oluşur. Bu tür laboratuvarlarda yapacağınız alıştırmalarla becerilerinizi geliştirebilirsiniz ancak sınav kılavuzunu ve diğer mevcut hazırlık kaynaklarını da incelemenizi öneririz.
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This course offers hands-on practice with Cloud Data Fusion, a cloud-native, code-free, data integration platform. ETL Developers, Data Engineers and Analysts can greatly benefit from the pre-built transformations and connectors to build and deploy their pipelines without worrying about writing code. This Quest starts with a quickstart lab that familiarises learners with the Cloud Data Fusion UI. Learners then get to try running batch and realtime pipelines as well as using the built-in Wrangler plugin to perform some interesting transformations on data.
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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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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Giriş düzeyindeki Google Cloud'da Makine Öğrenimi API'leri İçin Veri Hazırlama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Dataprep by Trifacta ile veri temizleme, Dataflow'da veri ardışık düzenleri çalıştırma, Managed Service for Apache Spark'ta küme oluşturma ve Apache Spark işleri çalıştırma ve makine öğrenimi API'lerini (Cloud Natural Language API, Google Cloud Speech-to-Text API ve Video Intelligence API dahil olmak üzere) çağırma.
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Giriş düzeyindeki Compute Engine İçin Cloud Load Balancing'i Uygulama beceri rozetini tamamlayarak şu konulardaki becerilerinizi gösterin: Compute Engine'de sanal makineler oluşturma ve dağıtma. Ağ ve uygulama yük dengeleyicileri yapılandırma.
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Giriş düzeyindeki BigQuery Verilerinden Analiz Elde Etme beceri rozetini alarak şu konulardaki becerilerinizi gösterin: SQL sorguları yazma, herkese açık tabloları sorgulama, örnek verileri BigQuery'ye yükleme, BigQuery'deki sorgu doğrulayıcı ile yaygın söz dizimi sorunlarını giderme ve BigQuery verilerine bağlanarak Data Studio'da rapor oluşturma.
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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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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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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. 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. 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 this quest to receive an exclusive Google Cloud digital badge.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. 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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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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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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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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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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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.
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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory course, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.
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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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Büyük veri, makine öğrenimi ve yapay zeka bilişim alanında günümüzün popüler konularıdır. Ancak bu alanlar yüksek uzmanlık gerektirir ve giriş seviyesi eğitim materyalleri zor bulunur. Neyse ki Google Cloud, bu alanlarda kullanıcı dostu hizmetler sunuyor ve bu giriş seviyesi kurs sayesinde BigQuery, Cloud Speech API ve Video Intelligence gibi araçlarla ilk adımlarınızı atmanıza imkan tanıyor.
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Bu giriş düzeyindeki kursta, Google Cloud'un temel araçları ve hizmetleri hakkında uygulamalı alıştırmalar yapacaksınız. Laboratuvarlarda işlenen kavramlara ilişkin daha fazla bağlam bilgisi ve inceleme sunmak amacıyla isteğe bağlı videolar paylaşılmaktadır. Google Cloud'un Temel Özellikleri, Google Cloud'u kullanmayı öğrenenler için önerilen ilk kurslardandır. Bulut teknolojisi konusunda çok az bilginiz olsa ya da hiç bilginiz olmasa bile bu kursa katılabilir ve ilk Google Cloud projenizde uygulayabileceğiniz pratik deneyimler kazanabilirsiniz. Cloud Shell komutları yazmak ve ilk sanal makinenizi dağıtmaktan Kubernetes Engine üzerinde veya yük dengelemeyi kullanarak uygulama çalıştırmaya kadar birçok konuyu kapsayan Google Cloud'un Temel Özellikleri, platformun temel özelliklerine dair mükemmel bir giriş niteliğindedir.
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Workspace is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level course you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.
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