가입 로그인

Jared Wong

회원 가입일: 2017

실버 리그

2300포인트
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant Earned 1월 29, 2022 EST
머신러닝 소개: 언어 처리 Earned 1월 23, 2022 EST
Intermediate ML: TensorFlow on Google Cloud Earned 1월 16, 2022 EST
머신러닝용 BigQuery Earned 10월 11, 2020 EDT
DEPRECATED BigQuery for Data Analysis Earned 10월 16, 2019 EDT
DEPRECATED BigQuery for Marketing Analysts Earned 10월 16, 2019 EDT
Google Workspace 필수 정보 Earned 7월 15, 2019 EDT
DEPRECATED Exploring APIs Earned 7월 15, 2019 EDT
Big Data on AWS Earned 6월 17, 2019 EDT
Advanced Operations Using Amazon Redshift Earned 6월 17, 2019 EDT
Databases on AWS for Windows Earned 5월 21, 2019 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned 4월 24, 2019 EDT
[DEPRECATED] Deploying Applications Earned 1월 21, 2019 EST
Machine Learning APIs Earned 12월 7, 2018 EST
[DEPRECATED] Data Engineering Earned 12월 2, 2018 EST
Scientific Data Processing Earned 11월 25, 2018 EST
DEPRECATED Application Development - Python Earned 11월 25, 2018 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned 11월 22, 2018 EST
기준: 데이터, ML, AI Earned 11월 20, 2018 EST
Cloud SQL Earned 11월 18, 2018 EST
Data Science on Google Cloud Earned 11월 5, 2018 EST
[Deprecated] Data Science on Google Cloud: Machine Learning Earned 10월 31, 2018 EDT

With Google Assistant part of over a billion consumer devices, this quest teaches you how to build practical Google Assistant applications integrated with Google Cloud services via APIs. Example apps will use the Dialogflow conversational suite and the Actions and Cloud Functions frameworks. You will build 5 different applications that explore useful and fun tools you can extend on your own. No hardware required! These labs use the cloud-based Google Assistant simulator environment for developing and testing, but if you do have your own device, such as a Google Home or a Google Hub, additional instructions are provided on how to deploy your apps to your own hardware.

자세히 알아보기

모두 알다시피 머신러닝은 빠르게 성장 중인 기술 분야 중 하나입니다. Google Cloud Platform(GCP)은 이러한 발전을 촉진하는 데 중요한 역할을 했습니다. GCP는 다양한 API를 통해 거의 모든 머신러닝 작업에 적합한 도구를 제공합니다. 이 초급 과정에서는 실무형 실습을 통해 머신러닝을 언어 처리에 적용하는 방법을 알아봅니다. 실습에 참여하여 텍스트에서 항목을 추출하고 감정 및 구문 분석을 수행하며 스크립트 작성에 Speech-to-Text API를 사용해 보세요.

자세히 알아보기

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.

자세히 알아보기

SQL만으로 몇 시간이 아닌 몇 분 만에 머신러닝 모델을 빌드하고 싶으신가요? BigQuery ML은 데이터 분석가가 기존 SQL 도구와 기술을 사용하여 머신러닝 모델을 만들고, 학습시키고, 평가하고, 예측할 수 있게 하여 머신러닝을 범용화합니다. 이 실습 시리즈에서는 다양한 모델 유형을 실험하고 좋은 모델을 만드는 요소를 알아봅니다.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

Workspace는 Google Cloud에서 제공하는 Google의 공동작업 애플리케이션 플랫폼입니다. 이 초급 과정에서는 사용자 관점에서 Workspace의 핵심 애플리케이션을 직접 실습해 볼 수 있습니다. Workspace에는 이 과정에서 다루는 것보다 훨씬 더 많은 애플리케이션과 도구 구성요소가 있지만, Gmail, Calendar, Sheets를 비롯한 주요 앱을 직접 사용해볼 수 있습니다. 각 실습은 10~15분 내에 완료할 수 있지만, 애플리케이션을 자유롭게 탐색해 볼 수 있도록 추가 시간이 제공됩니다.

자세히 알아보기

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.

자세히 알아보기

Scientists, developers, and other technologists from many different industries are taking advantage of AWS to perform big data analytics and meet the challenges of the increasing volume, variety, and velocity of digital information. AWS offers a portfolio of cloud computing services to help you manage big data by reducing costs, scaling to meet demand, and increasing the speed of innovation. In this quest, you’ll learn to work with advanced services for Big Data.

자세히 알아보기

In this Quest, you will delve deeper into the uses and capabilities of Amazon Redshift. You will use a remote SQL client to create and configure tables, and gain practice loading large data sets into Redshift. You will explore the effects of schema variations and compression. You will explore visualization of Redshift data, and connect Redshift with Amazon Machine Learning to create a predictive data model.

자세히 알아보기

Learn how to deploy and administer databases running on Microsoft Windows Server in Amazon EC2 and Amazon RDS.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.

자세히 알아보기

In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.

자세히 알아보기

빅데이터, 머신러닝, 인공지능은 오늘날 인기 있는 컴퓨팅 관련 주제이지만 매우 전문화된 분야이기 때문에 초급용 자료를 구하기 어렵습니다. 다행히도 Google Cloud는 이러한 분야에서 사용자 친화적인 서비스를 제공하며 초급 과정을 통해 학습자에게 BigQuery, Cloud Speech API, Video Intelligence와 같은 도구를 사용해 시작할 기회를 제공합니다.

자세히 알아보기

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.

자세히 알아보기

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.

자세히 알아보기

This is the second of two courses of hands-on labs 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 second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

자세히 알아보기