Amazon Web Services (AWS) Certificate Online

Start your cloud computing journey with AWS certificate courses for beginners and keep up with the fast pace of innovation.

Franklin University has partnered with Coursera Campus to provide cutting-edge certificates to learners seeking to advance. Courses are open to all learners. No application required.

What You Will Learn

• Get to know AWS and build a foundational understanding of AWS services and how they apply to the real world of business
• Learn the basics of AWS infrastructure: computing, databases, networking, analytics, IoT, security, mobile, monitoring and more
• Understand AWS security policies, models, architecture and operational processes
• Explore various AWS threat management and data storage solutions

About the Amazon Web Services (AWS) Professional Certificate

The Amazon Web Services (AWS) Professional Certificate is for ambitious IT newbies and pros who want to grow their cloud skills and authenticate their technical expertise. This specialization offers a high-level look at the capabilities and uses of AWS cloud computing services.

The four courses that comprise the AWS Professional Certificate will help you build an overall understanding of the AWS cloud, preparing you for a new career or an advanced role as a cutting-edge cloud practitioner. You’ll acquire knowledge and experience in this highly regarded platform, giving you a competitive advantage in the workforce.

Whether you’re looking to start your career, tackle a new project or become a go-to expert, this Professional Certificate program is a great way to demonstrate to potential employers, clients and colleagues that you are on your way to getting valuable know-how in the AWS cloud platform.

During your self-paced studies, you’ll acquire a better understanding of the features within the world’s most adopted cloud platform, AWS. You’ll also learn how organizations around the world use AWS to migrate from transitional IT infrastructure securely to the cloud, along with the fundamentals of building serverless applications. These certificate courses also include hands-on lab practices and exercises developed by AWS technical instructors, so you can apply what you’ve learned to validate your skills and knowledge.

This Professional Certificate program is ideal anyone wanting to build credibility in being able to support cloud initiatives using AWS. You can expect this course to be the first step in preparing yourself for a variety of IT professional roles, including data analyst, data engineer and cloud native developer.

Required Amazon Web Services (AWS) Certificate Courses

Google Cloud IAM and Networking for AWS Professionals

INTERMEDIATE | Computer Science | Self-paced | 4 hours

This is the first course of a four-course series for cloud architects and engineers with existing AWS knowledge, and it compares Google Cloud and AWS solutions and guides professionals on their use. This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in AWS to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.

AWS Cloud Technical Essentials

BEGINNER | Information Technology | Self-paced | 25 hours

Are you in a technical role and want to learn the fundamentals of AWS? Do you aspire to have a job or career as a cloud developer, architect, or in an operations role? If so, AWS Cloud Technical Essentials is an ideal way to start. This course was designed for those at the beginning of their cloud-learning journey - no prior knowledge of cloud computing or AWS products and services required! Throughout the course, students will build highly available, scalable, and cost effective application step-by-step. Upon course completion, you will be able to make an informed decision about when and how to apply core AWS services for compute, storage, and database to different use cases. You’ll also learn about cloud security with a review of AWS' shared responsibility model and an introduction to AWS Identity and Access Management (IAM). And, you’ll know how AWS services can be used to monitor and optimize infrastructure in the cloud. AWS Cloud Technical Essentials is a fundamental-level course and will build your competence, confidence, and credibility with practical cloud skills that help you innovate and advance your professional future. Enroll in AWS Cloud Technical Essentials and start learning the technical fundamentals of AWS today! Note: This course was designed for learners with a technical background. If you are new to the cloud or come from a business background, we recommend completing AWS Cloud Practitioner Essentials (https://www.coursera.org/learn/aws-cloud-practitioner-essentials) before enrolling in this course.

AWS Fundamentals: Addressing Security Risk

BEGINNER | Information Technology | Self-paced | 6 hours

Welcome to AWS Fundamentals: Addressing Security Risk! This course will give you an overview of security best practices when developing and managing applications on AWS. The course, created by AWS experts and delivered by AWS Solutions Architects, uses video lectures, hands-on exercise guides, demonstrations, and quizzes to dive deep on concepts about security on AWS. You will learn about the concept of “least privilege” and the AWS Shared Responsibility Model, as well as view demonstrations of Amazon GuardDuty, AWS Secrets Manager, and cross-account access. We encourage IT professionals and leaders who are interested in learning about best practices for securing an AWS environment and applications developed on AWS to enroll in this course. Before taking this course, we recommend you complete AWS Fundamentals: Going Cloud Native (https://www.coursera.org/learn/aws-fundamentals-going-cloud-native). Class forums will also allow you to ask questions and interact with AWS training instructors.

AWS Fundamentals: Building Serverless Applications

BEGINNER | Information Technology | Self-paced | 11 hours

This course will introduce you to Amazon Web Services (AWS) serverless architecture. Through demonstrations and hands-on exercises you'll learn skills in building and deploying serverless solutions. Using real-world examples of a serverless website and chat bot, you'll build upon your existing knowledge of the AWS cloud to take advantage of the benefits of modern architectures for greater agility, innovation, and lower total cost of ownership across a range of AWS services, including AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon Lex. In this course, you'll have the option to complete hands-on exercises. We strongly suggest you take advantage of the AWS Free Tier to follow the lab exercises provided by throughout the course. Be aware that any services used outside of the free tier may result in you incurring charges through your AWS account. Class forums will also allow you to ask questions and interact with AWS training instructors. After completing this course, you'll have the knowledge and skills to build serverless solutions on AWS. This course has been developed by AWS, and is delivered by AWS technical instructors who teach cloud computing courses around the globe.

AWS: Publish a NodeJS Website from Scratch

BEGINNER | Information Technology | Self-paced | 2 hours

In this project-based course, you will learn how to launch your own server at Amazon AWS Lightsail, configure your own SSH client and upload files to the server. Then, you will learn how to modify the server to access it privately. Further, you will learn how to change the server configuration to have a simple NodeJS game with both a backend and frontend started automatically and its content served through the Apache webserver. Note: To avoid distraction for setup during the course, we would recommend that you create an Amazon AWS account beforehand.

Using TensorFlow with Amazon Sagemaker

ADVANCED | Data Science | Self-paced | 2 hours

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy an image classifier created and trained with the TensorFlow framework within the Amazon Sagemaker ecosystem. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. However, it is possible to use Sagemaker for custom training scripts as well. We will use TensorFlow and Sagemaker's TensorFlow Estimator to create, train and deploy a model that will be able to classify images of dogs and cats from the popular Oxford IIIT Pet Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based image classification, but will focus purely on training and deploying a model with Sagemaker and TensorFlow. You will also need to have some experience with Amazon Web Services (AWS). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Object Detection with Amazon Sagemaker

ADVANCED | Data Science | Self-paced | 2 hours

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy an object detector using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the SSD Object Detection algorithm from Sagemaker to create, train and deploy a model that will be able to localize faces of dogs and cats from the popular IIIT-Oxford Pets Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based SSD or Object Detection, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Image Classification with Amazon Sagemaker

ADVANCED | Data Science | Self-paced | 2 hours

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy an image classifier using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the image classification algorithm from Sagemaker to create, train and deploy a model that will be able to classify 37 breeds of dogs and cats from the popular IIIT-Oxford Pets Dataset. Since this is a practical, project-based course, we will not dive in the theory behind deep learning based image classification, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Semantic Segmentation with Amazon Sagemaker

ADVANCED | Data Science | Self-paced | 2 hours

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will learn how to train and deploy a Semantic Segmentation model using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background). Since this is a practical, project-based course, we will not dive in the theory behind deep learning based semantic segmentation, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS).

Frequently Asked Questions