Google Cloud Courses Collection

Google Cloud helps millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in and for the cloud.

The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables enterprise customers and partners to use Google products and solution offerings in an effective and impactful way. In this post,we take look at curated compilation of Google Cloud courses.

Quick Snapshot

Business Strategy
- #1.Advanced Machine Learning with TensorFlow on Google Cloud Platform
Computer Security and Networks
Design and Product
Mobile and Web Development
Software Development
Data Analysis
- #32.Google Cloud Platform Fundamentals: Core Infrastructure
Machine Learning
Cloud Computing
- #48.Sequence Models for Time Series and Natural Language Processing
- #49.Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Data Management
Networking
Security
Support and Operations
Certification
- #66.Preparing for the Google Cloud Associate Cloud Engineer Exam
Gsuite
#Useful Resources

Pre-requisites : Proficiency with with command-line tools and Linux operating system environments and systems operations experience (including deploying and managing applications, either on-premises or in a public cloud environment) is helpful in understanding the technologies covered.

Business Strategy

#1.Advanced Machine Learning with TensorFlow on Google Cloud Platform

Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.

Computer Security and Networks

#2.Architecting with Google Cloud Platform

Google Cloud
This specialization introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. This class is intended for the following participants: â— Cloud Solutions Architects, DevOps Engineers. â— Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. This Specialization has updated, improved content that will help you if you are preparing for the Google Certified Professional – Cloud Architect exam as well as the Associate Cloud Engineer exam. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#3.Data Engineering, Big Data, and Machine Learning on GCP

Google Cloud
This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches the following skills: Design and build data processing systems on Google Cloud Platform Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML Enable instant insights from streaming data This class is intended for developers who are responsible for: Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#4.Developing APIs with Google Cloud’s Apigee API Platform

Google Cloud
This three-course specialization will introduce you to the many out of box capabilities of the Google Cloud Platform (Apigee) and how to apply them to your APIs to properly implement and secure them. Through a combination of video lectures, hands on labs, and supplemental materials, you’ll learn how to design, build, and deploy your API solution using services on the Google Apigee Platform. This specialization is intended for API designers and developers and is considered to be fundamental. We highly recommend this training prior to working on the Google Apigee Platform This specialization will allow you to spin up your own free environment and develop your first set of APIs as the instructor walks you through a specific real world scenario. Note: If you wish to become Apigee certified, please refer to the following URL for more information: https://apigee.com/api-management/#/certification

#5.Developing Applications with Google Cloud Platform

Google Cloud
In this specialization, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. This class is intended for application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform. This course teaches participants the following skills: Use best practices for application development. Choose the appropriate data storage option for application data. Implement federated identity management. Develop loosely coupled application components or microservices. Integrate application components and data sources. Debug, trace, and monitor applications. Perform repeatable deployments with containers and deployment services. Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment.

#6.From Data to Insights with Google Cloud Platform

Google Cloud
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: â— Data Analysts, Business Analysts, Business Intelligence professionals â— Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#7.Install and Manage Google Cloud’s Apigee API Platform

Google Cloud
This three-course specialization introduces you to the design principles, installation steps and operational procedures required to successfully adopt the Apigee API Platform On-Premises. Through a combination of video lectures, hands on labs, and supplemental materials, you’ll learn how to design Apigee topologies, install, manage and upgrade platform. As well as how to conduct post installation and recurrent activities corresponding to security, monitoring, scaling and troubleshooting. This specialization is intended for Operations Engineers and Architects responsible for the installation and management of Apigee API Platform On-Premises.

#8.Machine Learning with TensorFlow on Google Cloud Platform

Google Cloud
What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <

#9.Networking in Google Cloud Platform

Google Cloud
This specialization gives participants broad study of core infrastructure and networking options on Google Cloud Platform. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN. The specialization will also cover common network design patterns and automated deployment using Deployment Manager. This class is intended for the following participants: â— Network Engineers and Admins who are either using Google Cloud Platform or are planning to do so â— Individuals who want to be exposed to software-defined networking solutions in the cloud. To get the most out of this course, participants should have: *Prior understanding of the OSI 7-layer model *Prior understanding of IPv4 addressing *Prior experience with managing IPv4 routes

#10.API Design and Fundamentals of Google Cloud’s Apigee API Platform

Google Cloud
This course gives you an introductory look at the Apigee API Platform and API Design in general. We’ll cover topics such as how to properly navigate through the Apigee Edge UI as well as how to approach API design and ensure industry best practices are followed. We’ll also go over the Apigee Technology Stack to ensure a full understanding of all the platform components. By taking this course, you’ll come to have a high level understanding of APIs and will lay the foundation for the more detailed deep dive courses later in the specialization. All supplemental materials will be be provided for offline review. If you ever wanted to learn about Apigee and APIs in general, this is the course for you!

#11.API Development on Google Cloud’s Apigee API Platform

Google Cloud
This course gives you an in depth overview of API development on the Apigee API Platform. We’ll learn abut how to properly approach your API development and ensure you’re aware of the various tools and out of the box policies available within Apigee Edge that can assist you with implementation. As part of the lab exercises, we’ll provide a set of instructions to spin up your own Apigee free environment and start developing a basic API from scratch. By taking this course, you’ll come to have a high level understanding of API development. All supplemental materials will be provided to you for offline review / reference. If you ever wanted to learn about API development, this is the course for you! Note: Those taking this course should first complete the “API Design and Fundamentals of Google Cloud’s Apigee API Platform” course.

#12.API Security on Google Cloud’s Apigee API Platform

Google Cloud
Now that your APIs are developed, it’s time to talk about how to secure them. This course covers API security using the Apigee API platform. We’ll learn about how to properly secure your APIs by covering topics such as the types of OAuth, TLS, and SAML to name a few. We’ll apply these concepts with hands on labs to implement proper authentication and validation in your APIs. By taking this course, you’ll come to have a high level understanding of API security and why it’s important. All supplemental materials will be provided to you for offline review / reference. Since this is the final course in the Specialization, you’ll come out of this course with a fully working and secure API to be used as reference for all your future builds! Note: Before starting this course, please ensure that you have completed the “API Design and Fundamentals of Google Cloud’s Apigee API Platform” and “API Development on Google Cloud’s Apigee API Platform” courses.

#13.Achieving Advanced Insights with BigQuery

Google Cloud
The third course in this specialization is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#14.App Deployment, Debugging, and Performance

Google Cloud
In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance. Prerequisites and prework Completed Google Cloud Platform Fundamentals or have equivalent experience Working knowledge of Node. js Basic proficiency with command-line tools and Linux operating system environments Previous course(s) in the specialization

#15.Applying Machine Learning to your Data with GCP

Google Cloud
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This 1-week, accelerated online course teaches participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. PREREQUISITES To get the most out of this course, participants must complete the prior courses in this specialization: Exploring and Preparing your Data Storing and Visualizing your Data Architecture and Performance >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#16.Art and Science of Machine Learning

Google Cloud
Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance. In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.

#17.Building Resilient Streaming Systems on Google Cloud Platform

Google Cloud
This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you’ll learn how to build streaming data pipelines using Google Cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audience. Prerequisites: Google Cloud Platform Big Data and Machine Learning Fundamentals (or equivalent experience) Some knowledge of Java Objectives: Understand use-cases for real-time streaming analytics Use Google Cloud PubSub asynchronous messaging service to manage data events Write streaming pipelines and run transformations where necessary Get familiar with both sides of a streaming pipeline: production and consumption Interoperate Dataflow, BigQuery and Cloud Pub/Sub for real-time streaming and analysis

#18.Creating New BigQuery Datasets and Visualizing Insights

Google Cloud
This is the second course in the Data to Insights specialization. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#19.Develop and Deploy Windows Applications on Google Cloud Platform

Google Cloud
Learn to deploy and run Microsoft WindowsÂ® applications on Google Cloud Platform (GCP). Through lectures and hands-on labs, learn how to configure and run Microsoft Windows and Microsoft SQL Server in Google Compute Engine. You will also learn how to develop and deploy ASP.NET applications and deploy them to Google Compute Engine, Google App Engine, and Google Container Engine. Course objectives This course teaches participants the following skills: Configuring Microsoft Windows and Microsoft SQL Server in Google Compute Engine Deploying ASP.NET MVC applications to Google Compute Engine Deploying .NET Core applications to Google Compute Engine, Google Compute Engine, and Google Container Engine Pre-requisites System-administration or application-development experience with Microsoft Windows A general familiarity with cloud computing

Design and Product

#20.Elastic Cloud Infrastructure: Containers and Services

Google Cloud
In this course, learners explore services provided to applications built on GCP that enhance their scalability and maintainability. They work with services like Google Cloud Pub/Sub and Google Cloud Functions to make applications more efficient. They explore the use of containers on GCP. The course concludes with a review of the specialization. Prerequisites: To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals (Core Infrastructure or AWS Professionals.) or have equivalent experience Completed Essential Cloud Infrastructure: Foundation or have equivalent experience Completed Essential Cloud Infrastructure: Core Services or have equivalent experience Completed Elastic Cloud Infrastructure: Scaling and Automation or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#21.Elastic Cloud Infrastructure: Scaling and Automation

Google Cloud
This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services. Prerequisites: To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals (Core Infrastructure or AWS Professionals.) or have equivalent experience Completed Essential Cloud Infrastructure: Foundation or have equivalent experience Completed Essential Cloud Infrastructure: Core Services or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#22.End-to-End Machine Learning with TensorFlow on GCP

Google Cloud
In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www. coursera.org/specializations/machine-learning-tensorflow-gcp). One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#23.Essential Cloud Infrastructure: Core Services

Google Cloud
This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Prerequisites: To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals (Core Infrastructure or AWS Professionals.) or have equivalent experience Completed Essential Cloud Infrastructure: Foundation or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

Mobile and Web Development

#24.Essential Cloud Infrastructure: Foundation

Google Cloud
This 1-week, accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud Platform through the console and Cloud Shell. You’ll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules. Prerequisites To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals (Core Infrastructure or AWS Professionals.) or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment

#25.Exploring and Preparing your Data with BigQuery

Google Cloud
Welcome to the Coursera specialization, From Data to Insights with Google Cloud Platform brought to you by the Google Cloud team. I’m Evan Jones (a data enthusiast) and I’m going to be your guide. This first course in this specialization is Exploring and Preparing your Data with BigQuery. Here we will see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud Platform. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Cloud Dataprep to analyze and transform your datasets. This course should take about one week to complete, 5-7 total hours of work. By the end of this course, you’ll be able to query and draw insight from millions of records in our BigQuery public datasets. You’ll learn how to assess the quality of your datasets and develop an automated data cleansing pipeline that will output to BigQuery. Lastly, you’ll get to practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#26.Feature Engineering

Google Cloud
Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models. In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future data science projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#27.G Suite Administrator Fundamentals

Google Cloud
G Suite Administrator Fundamentals serves as the starting place for any new G Suite admin as they begin their journey of managing and establishing G Suite best practices for their organization. This 15-30 hour accelerated one week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more. Through a series of in-product training lessons, step-by-step hands-on exercises, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as G Suite administrators. Learning Objectives By the end of this course participants will be able to: Describe the basic organizational benefits to using G Suite services for collaboration and technical deployment. Setup G Suite Accounts, and access and navigate the G Suite Admin Console. Provision and manage their G Suite users Add, use, and manage G Suite organizations Manage G Suite services such as Calendar, Gmail, and Drive Establish fundamental security practices Find help center resources and further resources to help guide the new admin after the training. Prerequisites To get the most out of this training course, learners should be prepared to: Install and be ready to use the latest version of Chrome web browser available at https://www. google.com/chrome/ Use an existing domain or purchase a new domain through a registrar such as Google, GoDaddy, enom, etc.

Software Development

#28.Getting Started With Application Development

#29.Getting Started with Google Kubernetes Engine

Google Cloud
This one-week, accelerated online class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates. At the end of the course, you will be able to: Understand container basics Containerize an existing application Understand Kubernetes concepts and principles Deploy applications to Kubernetes using the CLI Set up a continuous delivery pipeline using Jenkins Locate more documentation and training. To get the most of out of this course, learners should have basic proficiency with command-line tools and Linux operating system environments, as well as Web server technologies such as Nginx. We also recommend systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#30.Google Cloud Platform Big Data and Machine Learning Fundamentals

Google Cloud
This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Choose between Cloud SQL, BigTable and Datastore Train and use a neural network using TensorFlow Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: A common query language such as SQL Extract, transform, load activities Data modeling Machine learning and/or statistics Programming in Python Google Account Notes: Google services are currently unavailable in China.

#31.Google Cloud Platform Fundamentals for AWS Professionals

Google Cloud
This accelerated 6-hour course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. If you prefer to learn fast by doing, this course is for you. Learning Objectives This course teaches participants the following skills: â— Identify GCP counterparts for Amazon VPC, subnets, routes, NACLs, IGW, Amazon EC2, Amazon EBS, auto-scaling, Elastic Load Balancing, Amazon S3, Amazon Glacier, Amazon RDS, Amazon Redshift, AWS IAM, and more â— Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more â— Manage and monitor applications â— Explain feature and pricing model differences â— Locate documentation and training. Prerequisites To get the most of out of this course, participants should have basic proficiency with networking technologies like subnets and routing. Students are also expected to have experience with Amazon VPC, Amazon EC2 instances, and disks. Familiarity with Amazon S3 and AWS database technologies is recommended.

Data Analysis

#32.Google Cloud Platform Fundamentals: Core Infrastructure

Google Cloud
This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management. Hands-on labs give you foundational skills for working with GCP. Note: Google services are currently unavailable in China.

Machine Learning

#33.How Google does Machine Learning

Google Cloud
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently – of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#34.Image Understanding with TensorFlow on GCP

Google Cloud
This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

#35.Intro to TensorFlow

Google Cloud
We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf. estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine

#36.Introduction to Cloud Identity

Google Cloud
Introduction to Cloud Identity serves as the starting place for any new Cloud Identity, Identity/Access Management/Mobile Device Management admins as they begin their journey of managing and establishing security and access management best practices for their organization. This 15-30 hour accelerated, one-week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more. Through a series of introductory lessons, step-by-step hands-on exercises, Google knowledge resources, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as new Cloud Identity Administrators. Learning Objectives By the end of this course participants will be able to: Establish a Cloud Identity domain for their organization or personal domain. Add users in order to practice user lifecycle management. Modify user permissions to gain an understanding of core Cloud Identity features. Add mobile devices within the Google Mobile Management module. Modify mobile management policy sets to gain familiarity with product options. Navigate the Reports module, and practice running reports. Explore and apply different security protocols to the domain. Prerequisites To get the most out of this training course, learners should be prepared to: Sign up for a free 14 day trial of Cloud Identity. You will need to enter payment method information. We will show you step-by-step how to cancel your account if you wish to end your Cloud Identity instance at the end of training and avoid being charged. Use an existing domain or purchase a new domain through (if you do not have an existing domain, we will walk you through purchasing a domain through Google).

#37.Launching into Machine Learning

Google Cloud
Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives: Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets

#38.Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform

Google Cloud
This 1-week, accelerated course builds upon previous courses in the Data Engineering on Google Cloud Platform specialization. Through a combination of video lectures, demonstrations, and hands-on labs, you’ll learn how to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. You will also learn how to access various cloud storage options from their compute clusters and integrate Google’s machine learning capabilities into their analytics programs. In the hands-on labs, you will create and manage Dataproc Clusters using the Web Console and the CLI, and use cluster to run Spark and Pig jobs. You will then create iPython notebooks that integrate with BigQuery and storage and utilize Spark. Finally, you integrate the machine learning APIs into your data analysis. Pre-requisites Google Cloud Platform Big Data & Machine Learning Fundamentals (or equivalent experience) Some knowledge of Python

#39.Networking in GCP: Defining and Implementing Networks

Google Cloud
This self-paced training course gives participants broad study of networking options on Google Cloud Platform. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets and firewalls. The course also covers access control to networks, sharing networks and load balancing. To get the most out of this course, participants should have: *Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience *Prior understanding of the OSI 7-layer model *Prior understanding of IPv4 addressing *Prior experience with managing IPv4 routes >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#40.Networking in GCP: Hybrid Connectivity and Network Management

Google Cloud
This self-paced training course builds on the Networking in GCP: Defining and Implementing Networks course and enhances participants study of networking options on Google Cloud Platform. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy GCP networking technologies, such as the interconnection among networks, common network design patterns and the automated deployment of networks using Deployment Manager or Terraform. The course also covers networking pricing and billing to help you optimize your network spend and monitoring and logging features that can help you troubleshoot your GCP network infrastructure. To get the most out of this course, participants should have: *Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience *Completed Networking in GCP: Defining and Implementing Networks *Prior understanding of the OSI 7-layer model *Prior understanding of IPv4 addressing *Prior experience with managing IPv4 routes

#41.On Premises Capacity Upgrade and Monitoring with Google Cloud’s Apigee API Platform

Google Cloud
This course helps you build understanding on key considerations for capacity planning and how to add and remove logical and physical capacity to the platform. Here you will learn how to add and remove logical elements such as Organizations, Environments and Virtual Hosts. It will also cover how to add new and remove components as well as how to add entire regions. The course will also cover the Apigee API Platform’s monitoring capabilities, how to organize ideas for quickly detect and address failure scenarios as well as how to reach out to Apigee support for help.

#42.On Premises Installation and Fundamentals with Google Cloud’s Apigee API Platform

Google Cloud
This course provides an introduction to Apigee API Platform On-Premises. The material walks you over Apigee product capabilities overview, architecture characteristics, technology stack and the fundamentals of topology design. As part of the course key terminology, software organizational structure and architecture are covered. The course represents the foundation for your understanding of Apigee API Platform On-Premises. The knowledge built on this course is a must for your understanding of the rest of the material on this specialization. The second half of the course will focus on the installation of the Apigee API Platform on your on premise instance.

#43.On Premises Management, Security, and Upgrade with Google Cloud’s Apigee API Platform

Google Cloud
This course helps you build hands-on experience with Apigee API Platform On-Premises installation, management and upgrade processes. The material focuses on building understanding about the management capabilities of Apigee API Platform, how to secure the platform as well as how to maintained up to date by performing upgrades.

#44.Production Machine Learning Systems

Google Cloud
In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

#45.Recommendation Systems with TensorFlow on GCP

Google Cloud
In this course, you’ll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. Devise a content-based recommendation engine Implement a collaborative filtering recommendation engine Build a hybrid recommendation engine with user and content embeddings >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#46.Reliable Cloud Infrastructure: Design and Process

Google Cloud
This online course equips students to build highly reliable and efficient solutions on Google Cloud Platform, using proven design patterns and principles derived from Google Site Reliability Engineering (SRE). It is a continuation of the Architecting with Google Cloud Platform Specialization and assumes hands-on experience with the technologies covered in the other courses in the specialization. Through a combination of presentations, challenges, and hands-on labs, participants learn to design GCP deployments that are highly reliable and secure; and how to operate GCP deployments in a highly available and cost-effective manner. This course teaches participants the following skills: â— Design for high availability, scalability, and maintainability. â— Assess tradeoffs and make sound choices among Google Cloud Platform products. â— Integrate on-premises and cloud resources. â— Identify ways to optimize resources and minimize cost. â— Implement processes that minimize downtime, such as monitoring and alarming, unit and integration testing, production resilience testing, and incident post-mortem analysis. â— Implement policies that minimize security risks, such as auditing, separation of duties and least privilege. â— Implement technologies and processes that assure business continuity in the event of a disaster. Prerequisites â— Completion of prior courses in the Architecting with Google Cloud Platform Specialization or equivalent experience â— Basic proficiency with command-line tools and Linux operating system environments â— Systems Operations experience including deploying and managing applications, either on-premises or in a public cloud environment

#47.Securing and Integrating Components of your Application

Cloud Computing

#48.Sequence Models for Time Series and Natural Language Processing

Google Cloud
This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. Predict future values of a time-series Classify free form text Address time-series and text problems with recurrent neural networks Choose between RNNs/LSTMs and simpler models Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

#49.Serverless Data Analysis with Google BigQuery and Cloud Dataflow

Google Cloud
This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. Prerequisites: Google Cloud Platform Big Data and Machine Learning Fundamentals Experience using a SQL-like query language to analyze data Knowledge of either Python or Java Google Account Notes: Google services are currently unavailable in China.

Data Management

#50.Serverless Machine Learning with Tensorflow on Google Cloud Platform

Google Cloud
This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: â— Identify use cases for machine learning â— Build an ML model using TensorFlow â— Build scalable, deployable ML models using Cloud ML â— Know the importance of preprocessing and combining features â— Incorporate advanced ML concepts into their models â— Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: â— Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience â— Basic proficiency with common query language such as SQL â— Experience with data modeling, extract, transform, load activities â— Developing applications using a common programming language such Python â— Familiarity with Machine Learning and/or statistics Google Account Notes: Google services are currently unavailable in China.

#51.Security in Google Cloud Platform

Google Cloud
Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure GCP solution, including Cloud Identity, the GCP Resource Manager, Cloud IAM, Google Virtual Private Cloud firewalls, Google Cloud Load balancing, Cloud CDN, Cloud Storage access control technologies, Stackdriver, Security Keys, Customer-Supplied Encryption Keys, the Google Data Loss Prevention API, and Cloud Armor. Participants learn mitigations for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#52.Building Conversational Experiences with Dialogflow

Google Cloud
This course provides a deep dive into how to create a chatbot using Dialogflow, augment it with Cloud Natural Language API, and operationalize it using Google Cloud tools. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#53.Building Scalable Java Microservices with Spring Boot and Spring Cloud

Google Cloud
Microservices” describes a software design pattern in which an application is a collection of loosely coupled services. These services are fine-grained, and can be individually maintained and scaled. The microservices architecture is ideal for the public cloud, with its focus on elastic scaling with on-demand resources. In this course, you will learn how to build Java applications using Spring Boot and Spring Cloud on Google Cloud Platform. You’ll use Cloud Runtime Configuration and Spring Cloud Config to manage your application’s configuration. You’ll send and receive messages with Cloud Pub/Sub and Spring Integration. You’ll also use Cloud SQL as a managed relational database for your Java applications, and learn how to migrate to Cloud Spanner, which is Google Cloud’s globally-distributed strongly consistent database service. You’ll also learn about tracing and debugging your Spring applications with Stackdriver. To succeed in this course, you should be familiar with the Java programming language and building Java applications with tools such as Maven or Gradle. You should also have general knowledge of Google Cloud Platform >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<“

#54.Industrial IoT on Google Cloud Platform

Google Cloud
By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<< Welcome to the Coursera course, Industrial Internet of Things (IoT) on Google Cloud Platform (GCP) brought to you by the Google Cloud team. I’m Catherine Gamboa and I’m going to be your guide. This course covers the entire Industrial IoT network architecture from sensors and devices to analysis. The course discusses sensors and devices but the focus is on the cloud side. You’ll learn about the importance of scaling, device communication, and processing streaming data. The course uses simulated devices in the labs to allow you to concentrate on learning the cloud side of IIoT. The course is a little different than most Coursera courses because there is very little video. Most of the learning is done with short readings, quizzes, and labs. This course takes about two weeks to complete, 11-12 hours of work with 6 of those hours spent in labs. By the end of this course, you’ll be able to: create a streaming data pipeline, to create registries with Cloud IoT Core, topics and subscriptions with Cloud Pub/Sub, store data on Google Cloud Storage, query the data in BigQuery, and gain data insights with Dataprep. You’ll learn and practice these skills in 7 labs. Then you’ll have an opportunity to test yourself in an optional capstone lab using simulated devices or Cloud IoT Core Inspector.

#55.Managing Security in Google Cloud Platform

Google Cloud
This self-paced training course gives participants broad study of security controls and techniques on Google Cloud Platform. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure GCP solution, including Cloud Identity, the GCP Resource Manager, Cloud IAM, Google Virtual Private Cloud firewalls, Google Cloud Load balancing, Cloud CDN, Cloud Storage access control technologies, Stackdriver, Security Keys, Customer-Supplied Encryption Keys, the Google Data Loss Prevention API, and Cloud Armor. Participants learn mitigations for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. To get the most out of this course, participants should have: * Prior completion of Google Cloud Platform Fundamentals: Core Infrastructure or equivalent experience * Prior completion of GCP and Hybrid Networking Deep Dive or equivalent experience * Knowledge of foundational concepts in information security, such as * vulnerability, threat, attack surface * confidentiality, integrity, availability * common threat types and their mitigation strategies * public-key cryptography * public and private key pairs * certificates * cipher types * certificate authorities * Transport Layer Security/Secure Sockets Layer encrypted communication * public key infrastructures * security policy * Basic proficiency with command-line tools and Linux operating system environments * Systems Operations experience, deploying and managing applications, on-premises or in a public cloud environment * Reading comprehension of code in Python or Javascript >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

Networking

#56.Mitigating Security Vulnerabilities on Google Cloud Platform

#57.Preparing for the Google Cloud Professional Cloud Architect Exam

Google Cloud
From the course: “The best way to prepare for the exam is to be competent in the skills required of the job.” This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don’t know. And it helps you develop and practice skills required of practitioners who perform this job. The course follows the organization of the Exam Guide outline, presenting highest-level concepts, “touchstones”, for you to determine whether you feel confident about your knowledge of that area and its dependent concepts, or if you want more study. You also will learn about and have the opportunity to practice key job skills, including cognitive skills such as case analysis, identifying technical watchpoints, and developing proposed solutions. These are job skills that are also exam skills. You will also test your basic abilities with Activity Tracking Challenge Labs. And you will have many sample questions similar to those on the exam, including solutions. The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.

#58.Preparing for the Google Cloud Professional Data Engineer Exam

Security

#59.Site Reliability Engineering: Measuring and Managing Reliability

Google Cloud
This course teaches the theory of Service Level Objectives (SLOs), a principled way of describing and measuring the desired reliability of a service. Upon completion, learners should be able to apply these principles to develop the first SLOs for services they are familiar with in their own organizations. Learners will also learn how to use Service Level Indicators (SLIs) to quantify reliability and Error Budgets to drive business decisions around engineering for greater reliability. The learner will understand the components of a meaningful SLI and walk through the process of developing SLIs and SLOs for an example service.

#60.Machine Learning for Business Professionals

Google Cloud
This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that’s not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch. By the end of this course, you will have learned how to: Formulate machine learning solutions to real-world problems Identify whether the data you have is sufficient for ML Carry a project through various ML phases including training, evaluation, and deployment Perform AI responsibly and avoid reinforcing existing bias Discover ML use cases Be successful at ML You’ll need a desktop web browser to run this course’s interactive labs via Qwiklabs and Google Cloud Platform. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#61.Architecting with Google Kubernetes Engine: Foundations

Google Cloud
This course is part of a specialization focused on building efficient computing infrastructures using Kubernetes and Google Kubernetes Engine (GKE). The specialization introduces participants to deploying and managing containerized applications on GKE and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. The specialization also covers deploying practical solutions including security and access management, resource management, and resource monitoring. In this course, “Architecting with Google Kubernetes Engine: Foundations,” you get a review of the layout and principles of Google Cloud Platform, followed by an introduction to creating and managing software containers and an introduction to the architecture of Kubernetes. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

Support and Operations

#62.Business Transformation with Google Cloud

Google Cloud
What is cloud technology or data science and what’s all the hype about? More importantly, what can it do for you, your team, and your business? If you want to learn about cloud technology so you can excel in your role, help build the future of your business and thrive in the cloud era, then the Business Transformation with Google Cloud course is for you. Through this interactive training, you’ll learn about core cloud business drivers-specifically Google’s cloud-and gain the knowledge/skills to determine if business transformation is right for you and your team, and build short and long-term projects using the “superpowers” of cloud accordingly. You’ll also find several templates, guides, and resource links through the supplementary student workbook to help you build a custom briefing document to share with your leadership, technical teams or partners. Primary Audience: Business decision-makers: directors (managers of managers), managers of individual contributors (ICs) or ICs working in non-IT functions/divisions (such as finance, marketing, sales, HR, product design) interested in understanding the applications of Google’s cloud technology for business improvement opportunities and transformational project(s).

#63.Architecting with Google Kubernetes Engine

Google Cloud
The Architecting with Google Kubernetes Engine specialization will teach you how to implement solutions using Google Kubernetes Engine, or GKE, including building, scheduling, load balancing, and monitoring workloads, as well as providing for discovery of services, managing role-based access control and security, and providing persistent storage to these applications.

#64.Architecting with Google Kubernetes Engine: Production

Google Cloud
This course is part of a specialization focused on building efficient computing infrastructures using Kubernetes and Google Kubernetes Engine (GKE). The specialization introduces participants to deploying and managing containerized applications on GKE and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. The specialization also covers deploying practical solutions including security and access management, resource management, and resource monitoring. In this course, “Architecting with Google Kubernetes Engine: Production,” you’ll learn about Kubernetes and GKE security; logging and monitoring; and using GCP managed storage and database services from within GKE. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

#65.Architecting with Google Kubernetes Engine: Workloads

Google Cloud
This course is part of a specialization focused on building efficient computing infrastructures using Kubernetes and Google Kubernetes Engine (GKE). The specialization introduces participants to deploying and managing containerized applications on GKE and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. The specialization also covers deploying practical solutions including security and access management, resource management, and resource monitoring. In this course, “Architecting with Google Kubernetes Engine: Workloads,” you learn about performing Kubernetes operations; creating and managing deployments; the tools of GKE networking; and how to give your Kubernetes workloads persistent storage. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

Certification

#66.Preparing for the Google Cloud Associate Cloud Engineer Exam

Google Cloud
This one-week on-demand course helps prospective candidates structure their preparation for the Associate Cloud Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, demos and hands-on labs, candidates will familiarize themselves with the domains covered by the examination. This course by itself will not prepare a candidate to pass the Associate Cloud Engineer certification exam. It will, however, help the candidate better understand the areas covered by the exam and navigate the recommended resources provided by Google and Qwiklabs for preparing to take the exam, so they can formulate their own personal study plan. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms of service <<<

Gsuite

#67.Introduction to G Suite

Google Cloud
Introduction to G Suite is the first course in the G Suite Administration Specialization. The series will serve as the starting place for any new G Suite admin as they begin their journey of managing and establishing G Suite best practices for their organization. These courses together will leave you feeling confident to utilize the basic functions of the admin console to manage users, control access to services, configure security settings, and much more. Through a series of readings and step-by-step hands-on exercises, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as G Suite administrators. In this course you will sign up for a G Suite account and configure your DNS records for G Suite. You will learn how to provision and manage your users, and will create groups and calendar resources for your organization. You will be introduced to your Cloud Directory and will learn how to split your organization into organizational units to simplify user and service management. Finally you will learn how to delegate admin privileges to other users in your organization. Learning Objectives By the end of this course participants will be able to: – Setup a G Suite account and access and navigate the admin console. – Describe the key properties of the G Suite directory. – Provision users, groups and calendar resources in G Suite. – Undertake common user management tasks. – Explain how an organizational structure can be used in G Suite to simplify user and service management. – Describe the types of admin roles available in G Suite. – Use the G Suite Help Center as an aid to managing G Suite. Prerequisites – IMPORTANT PLEASE READ. To get the most out of this training course, learners should be prepared to: – Install and be ready to use the latest version of Chrome web browser available at https://www. google.com/chrome/ – Purchase a new domain through a registrar such as enom, GoDaddy. Note: If you already have a domain that you would like to use for the trial you can do this but this course does not provide detailed steps on how to associate an existing domain with a G Suite trial account. For detailed instructions on how to do that, please refer to this Help Center article: https://support. google.com/a/topic/9196 – Provide credit card details as part of the G Suite account setup. You will be using a 14 day trial G Suite Enterprise account during this course. As part of the sign up flow you will be required to provide credit card details. No charges for G Suite are made to your credit card until the trial period has ended. You must ensure that you CANCEL YOUR SUBSCRIPTION before the trial period ends to avoid and charges. This is very IMPORTANT so don’t forget!

#68.Managing G Suite

Google Cloud
Managing G Suite is the second course in the G Suite Administration Specialization. This course focuses on the G Suite core services such as Gmail, Calendar, and Drive & Docs. You will become familiar with the various service settings, and learn how to enable them for all or just a subset of your users. You will gain an understanding of Google Vault, Google’s ediscovery service. You will understand the various admin console reports that are available and be able to search and filter the information in these reports. Finally you will see how multiple domains can be used with G Suite and learn how to add a new domain to your account. Learning Objectives By the end of this course participants will be able to: – Enable and disable G Suite services for different parts of the organization. – Configure common settings for G Suite core services such as Gmail, Calendar, and Drive and Docs. – Understand the mobile device management options available in G Suite. – Describe Google Vault and learn how to use it to retain, search and export your organization’s data. – Navigate and interpret G Suite admin reports and setup administrator alerts. – Explain the basics of multi domain management within G Suite. Prerequisites You should have completed the first course in the series: Introduction to G Suite.

#69.G Suite Administration

Google Cloud
The G Suite Administration Specialization has been developed to help administrators master the foundations of managing and establishing G Suite best practices for their organization. You will setup and configure a new G Suite account, and explore options for provisioning users, groups and resources. You will learn how to manage your users and also become familiar with organizational structures and G Suite core services such as Gmail, Calendar, and Drive & Docs. You will learn how to configure these services to meet your particular business needs for different parts of the organization. In the security module you will be introduced to Google’s best practices to protect your users and data. You will examine user and application security and become familiar with the Single Sign On (SSO) options available for your organization. You will be able to use the tools provided to identify security events and risks and mitigate problems that may arise. Finally you will look at G Suite mail management. In this module you will configure email compliance and implement measures to protect your organization from spam, spoofing, phishing and malware attacks. You will also become familiar with the various mail routing options available.

#70.G Suite Security

Google Cloud
G Suite Security is is the third course in the G Suite Administration Specialization. In this course you will focus on the various aspects of G Suite Security including user password policies and how to enable and enforce two step verification (2SV) for your users. You will learn about application security and understand how to whitelist and block API access to your account. You will see how G Suite can easily be integrated with a number of predefined 3rd party applications. You will also become familiar with the SSO options in G Suite. Finally you will understand how to spot potential security risks within your organization and learn how to address them using the tools available in the admin console. Learning Objectives By the end of this course participants will be able to: – Configure Google’s default user protection settings such as password policies and recovery options. – Understand best practices for implementing and enforcing 2-step verification in your organization. – Understand the SSO options available and be able to identify the differences between using Google as an Identity Provider versus a 3rd party provider. – Be able to integrate cloud based enterprise SAML applications into your G Suite account using Google as the Identity Provider. – Understand how to integrate your own LDAP compliant applications into G Suite using the Secure LDAP service. – Restrict access to a Google service to trusted applications only to prevent malicious attacks on that service. – Manage the G Suite Marketplace for your organization to ensure only trusted applications can be installed on your devices. – Use the security and alert centers to identify, triage, and take action on security and privacy issues in your organization. Prerequisites You should have completed the first two courses in the series: Introduction to G Suite, and Managing G Suite.

#71.G Suite Mail Management

Google Cloud
G Suite Mail Management is the final course in the G Suite Administration Specialization. In this course you will learn how to protect your organization against spam, spoofing, phishing and malware attacks. You will configure email compliance and learn how to implement data loss prevention (DLP) for your organization. You will gain an understanding of the mail routing options available and learn how to whitelist and block senders. You will also become familiar with other mail options such as inbound and outbound gateways, 3rd party email archiving, and journaling to Vault. Learning Objectives By the end of this course participants will be able to: – Gain a basic understanding of the Domain Name System (DNS) used on internet and be able to identify the record types used by G Suite. – Be able to implement common email security measures in your DNS records such as SPF, DKIM and DMARC and be able to explain the purpose of each measure. – Learn how protect your users from inbound phishing and harmful software (malware). – Learn how to control which end user access features are available to your users. – Learn how to configure email whitelists, blacklists and approve sender lists to better manage mail delivery and protect against spam, phishing and malware. – Gain an understanding of the various compliance features provided in G Suite. – Gain a basic understanding of the mail routing options available to you as the G Suite administrator. Prerequisites You should have completed the other courses in the series: Introduction to G Suite, Managing G Suite, and G Suite Security.

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Google Cloud Courses Collection

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In this post,we take look at curated compilation of Google Cloud courses.

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Karthik

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