Microsoft Azure Certificate Online

Master the fundamentals of cloud services with Microsoft-certified Azure certificate courses that build your technical skills in supporting security, privacy and compliance in cloud computing.

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.

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Get unlimited access to over 7,000 offerings found on the Coursera website – including guided projects, specializations and professional certificates offered by hundreds of leading universities and companies. You also get access to all 39 professional certificates found in the Franklin Marketplace.

What You Will Learn

• Gain an understanding of the benefits and considerations of cloud computing and explore Azure's architectural components
• Learn the concepts behind artificial intelligence (AI) and machine learning (ML), including types and tasks
• Look at the importance of Big Data analytics and how to use analytical tools such as Python, R and Apache Spark
• Build an end-to-end machine learning pipeline in Azure ML Studio and train your computer to recognize images using AutoML

About the Microsoft Azure Professional Certificate

The growing field of cloud computing means there’s also a growing need for qualified Cloud computing professionals. The online Microsoft Azure Professional Certificate is designed for those with a desire for a career in cutting-edge cloud technologies. So, if you’re a self-starter with an innovative spirit and a passion for learning, this MS Azure certificate program is ideal for you.

Comprised of 11 self-paced online courses, the Microsoft Azure Professional Certificate will introduce you to the fundamentals of working with the Google Cloud Platform (GCP) and Microsoft Azure services. You’ll learn the basic concepts and foundational best practices of cloud computing and Software-as-a-Service (SaaS), as well as dig into data science with a look at deep learning, machine learning (ML) and predictive modeling.

You’ll also explore the in-demand tools used by Azure professionals, including BigQuery, Python, Apache Spark and Azure ML Studio. Plus, you’ll learn and practice a variety of data analytics and cloud-based tasks, such as building, training and deploying your own ML model.

Finally, you’ll gain relevant experience through a series of engaging hands-on projects ranging from creating custom R models on Azure ML Studio, training a neural network, building an end-to-end ML pipeline, and building a predictive model – without writing a single line of code.

Required Microsoft Azure Certificate Courses

Google Cloud IAM and Networking for Azure Professionals

INTERMEDIATE | Information Technology | Self-paced | 4 hours

This is the first course of a four-course series for cloud architects and engineers with existing Azure knowledge, and it compares Google Cloud and Azure 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 Azure 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.

Getting Started with Azure

BEGINNER | Information Technology | Self-paced | 20 hours

This course in an introduction to Microsoft Azure services. Students will gain familiarity with core Azure topics and practice implementation of infrastructure components.

Developing AI Applications on Azure

ADVANCED | Data Science | Self-paced | 16 hours

This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach. As you go through the course, we'll introduce you to Microsoft's pre-trained and managed machine learning offered as REST API's in their suite of cognitive services. We'll implement solutions using the computer vision API and the facial recognition API, and we'll do sentiment analysis by calling the natural language service. Using the Azure Machine Learning Service you'll create and use an Azure Machine Learning Worksace.Then you'll train your own model, and you'll deploy and test your model in the cloud. Throughout the course you will perform hands-on exercises to practice your new AI skills. By the end of this course, you will be able to create, implement and deploy machine learning models.

Cloud Computing Basics (Cloud 101)

BEGINNER | Information Technology | Self-paced | 9 hours

Welcome to Cloud Computing Basics (Cloud 101). Over the next few weeks, we will discuss the basics of Cloud computing: what it is, what it supports, and how it is delivered. We will delve into storage services, Cloud economics, levels of managed infrastructure, and Azure services. We will also explore different deployment models of Cloud computing, as well as several hosting scenarios. Last but not least, we will compare some of the cloud platforms and discuss the future of cloud computing.

Data Processing with Azure

INTERMEDIATE | Information Technology | Self-paced | 13 hours

This Azure training course is designed to equip students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as Python, R, and Apache Spark.

Azure Infrastructure Fundamentals

ADVANCED | Computer Science | Self-paced | 30 hours

Microsoft Azure is a service created by Microsoft to provide cloud computing for creating and managing applications and services using a cloud environment. Azure provides software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). The platform supports many programming languages and frameworks and can be used alone or in a multi-vendor cloud environment. This course focuses on the Fundamentals of Azure Infrastructure including infrastructure as a service. We’ll begin with understanding the subscription, configuring security and acquiring storage. Then you’ll build virtual machines and VNETS. Azure environments can be highly available and very resilient. Data can be backed up to the cloud for safety. These are the concepts we will discuss in this course.

Build Random Forests in R with Azure ML Studio

BEGINNER | Data Science | Self-paced | 2 hours

In this project-based course you will learn to perform feature engineering and create custom R models on Azure ML Studio, all without writing a single line of code! You will build a Random Forests model in Azure ML Studio using the R programming language. The data to be used in this course is the Bike Sharing Dataset. The dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information. Using the information from the dataset, you can build a model to predict the number of bikes rented during certain weather conditions. You will leverage the Execute R Script and Create R Model modules to run R scripts from the Azure ML Studio experiment perform feature engineering. This is the fourth course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

Deep Learning Inference with Azure ML Studio

BEGINNER | Data Science | Self-paced | 1 hour

In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits. This is the third course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

Machine Learning Pipelines with Azure ML Studio

BEGINNER | Data Science | Self-paced | 2 hours

In this project-based course, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual's income. It predicts whether an individual's annual income is greater than or less than $50,000. The estimator used in this project is a Two-Class Boosted Decision Tree classifier. Some of the features used to train the model are age, education, occupation, etc. Once you have scored and evaluated the model on the test data, you will deploy the trained model as an Azure Machine Learning web service. In just under an hour, you will be able to send new data to the web service API and receive the resulting predictions. This is the second course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

AutoML for Computer Vision with Microsoft Custom Vision

BEGINNER | Data Science | Self-paced | 2 hours

Welcome to this hands-on project on using Microsoft’s Custom Vision service for automated machine learning or AutoML as it’s popularly known. In this project, you are going to use Microsoft’s drag and drop tool to train your computer to recognize images of dogs and cats. We are going to do all of this without writing a single line of code! To take this guided-project, you do not need a background in computer science, machine learning or coding. The only prerequisite for this project is that you have a Microsoft Azure account. If you don’t already have one, you will have to sign up for it. 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.

Predictive Modelling with Azure Machine Learning Studio

BEGINNER | Data Science | Self-paced | 2 hours

In this project, we will use Azure Machine Learning Studio to build a predictive model without writing a single line of code! Specifically, we will predict flight delays using weather data provided by the US Bureau of Transportation Statistics and the National Oceanic and Atmospheric Association (NOAA). You will be provided with instructions on how to set up your Azure Machine Learning account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - 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.

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