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Kitty Cai

Member since 2021

Silver League

2900 points
NCAA® March Madness®: Bracketology with Google Cloud Earned Aug 14, 2021 EDT
Using the Cloud SDK Command Line Earned Aug 13, 2021 EDT
Google Workspace Essentials Earned Aug 12, 2021 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications Earned Aug 11, 2021 EDT
BigQuery for Machine Learning Earned Aug 9, 2021 EDT
Implementing Cloud Load Balancing for Compute Engine Earned Aug 9, 2021 EDT
Google Cloud Essentials Earned Aug 8, 2021 EDT

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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For everyone using Google Cloud Platform for the first time, getting familar with gcloud, Google Cloud's command line, will help you get up to speed faster. In this quest, you'll learn how to install and configure Cloud SDK, then use gcloud to perform some basic operations like creating VMs, networks, using BigQuery, and using gsutil to perform operations.

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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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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.

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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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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.

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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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