Using the Designer
Machine learning is a notoriously complex subject that usually requires a great deal of advanced math and software development skills. That’s why it’s so amazing that Azure Machine Learning lets you train and deploy machine learning models without any coding, using a drag-and-drop interface. With this web-based software, you can create applications for predicting everything from customer churn rates to image classifications to compelling product recommendations.
In this course, you will learn the basic concepts of machine learning and then follow hands-on examples of choosing an algorithm, running data through a model, and deploying a trained model as a predictive web service.
- Create an Azure Machine Learning workspace
- Train a machine learning model using the drag-and-drop interface
- Deploy a trained model to make predictions based on new data
- Anyone who is interested in machine learning
- General technical knowledge
- A Microsoft Azure account is recommended (sign up for free trial at https://azure.microsoft.com/free if you don’t have an account)
The GitHub repository for this course is at https://github.com/cloudacademy/azureml-intro.
Welcome to “Introduction to Azure Machine Learning”. My name’s Guy Hummel, and I’m a Microsoft Certified Azure Data Scientist. If you have any questions, feel free to connect with me on LinkedIn and send me a message, or send an email to firstname.lastname@example.org.
This course is intended for anyone who’s interested in machine learning. I’ll be showing you how to build machine learning models with a drag-and-drop interface without writing any code.
There are no prerequisites for this course. We’ll cover the basics of machine learning, so it’s okay if you haven’t worked with machine learning before. I do recommend that you have an Azure account, though, so you can follow along with the examples in this course and do them yourself. If you don’t already have one, then you can create a free trial account.
To save you the trouble of typing in the URLs shown in this course, I’ve created a GitHub repository with a readme file that contains all of the URLs. The link to the repository is at the bottom of the course overview below.
By the end of this course, you should be able to create an Azure Machine Learning workspace, train a machine learning model using the drag-and-drop interface, and deploy a trained model to make predictions based on new data.
We’d love to get your feedback on this course, so please give it a rating when you’re finished.
Now, if you’re ready to learn how to get the most out of Azure Machine Learning, then let’s get started.
Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).