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Apply your skills in Google Cloud console

Mohamed Sami

Member since 2019

Bronze League

1200 points
Google Workspace Essentials Earned Feb 27, 2020 EST
Machine Learning APIs Earned Feb 27, 2020 EST
Intermediate ML: TensorFlow on Google Cloud Earned Feb 27, 2020 EST
BigQuery for Machine Learning Earned Feb 27, 2020 EST
Baseline: Data, ML, AI Earned Feb 27, 2020 EST
Serverless Design with AWS Lambda Earned Feb 15, 2020 EST
Corporate Apps on AWS for Windows Earned Feb 15, 2020 EST
Programming Windows on AWS Earned Feb 15, 2020 EST
Security on AWS Earned Feb 15, 2020 EST
Compute & Networking Earned Feb 15, 2020 EST
Intro to ML: Language Processing Earned Feb 15, 2020 EST
Baseline: Deploy & Develop Earned Dec 27, 2019 EST

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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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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Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, Google Cloud provides user-friendly services in these areas, and with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API and Video Intelligence. Want extra help? 1-minute videos walk you through key concepts for each lab.

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In this Quest, you will learn how to write functions with the AWS Lambda Service that respond to events and integrate other AWS Services. You will create applications that write records to Amazon DynamoDB, send messages with Amazon SNS, and monitor events in Amazon CloudWatch and external services. You will even write a back-end function in Lambda for creating a voice-response app for Alexa and the Amazon Echo.

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Learn how to deploy Microsoft Windows Server-based applications in the AWS cloud, including Microsoft Exchange, Dynamics CRM and SharePoint.

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Learn how to develop applications in Microsoft Visual Studio leveraging AWS services.

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This quest is designed to teach you how to apply AWS Identity and Access Management, in concert with several other AWS Services, to address real-world application and service security management scenarios.

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In this quest, you’ll learn to work with services related to Compute and Networking, including Amazon EC2, Amazon Elastic Load Balancing, and Amazon Virtual Private Cloud (VPC).

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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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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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