Using Azure ML Studio
The course is part of this learning path
Machine learning is a notoriously complex subject, which usually requires a great deal of advanced math and software development skills. That’s why it’s so amazing that Azure Machine Learning Studio 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.
- Prepare data for use by an Azure Machine Learning Studio experiment
- Train a machine learning model in Azure Machine Learning Studio
- Deploy a trained model to make predictions
- Anyone who is interested in machine learning
- No mandatory prerequisites
- Azure account recommended (sign up for free trial at https://azure.microsoft.com/free if you don’t have an account)
This Course Includes
- 54 minutes of high-definition video
- Many hands-on demos
Welcome to “Introduction to Azure Machine Learning Studio”. My name’s Guy Hummel and I’ll be showing you how to build machine learning models with a drag-and-drop interface, without writing any code. I’m a Research Lead at Cloud Academy and I have over 10 years of experience with cloud technologies. 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.
There are no prerequisites for this course. We’ll start off with a basic introduction to machine learning concepts, 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 put them at the bottom of the Video Transcript tab below this video.
Machine learning is a hot topic these days. It’s constantly in the news. Here’s a small sample of some recent headlines.
It may be hard to believe that machine learning could be transforming everything from cybersecurity to customer service to music, but its benefits really are being explored in nearly every industry imaginable. All of this excitement makes machine learning sound almost magical. But if you look under the hood, it’s actually a rather simple concept.
At a very high level, here’s how it works. You feed lots of real-world data into a program and the program tries to make generalizations about the data. It then uses these generalizations to make predictions when it’s given new data.
For example, after looking at lots of x-rays of patients with and without cancer, it can then analyze an x-ray of a new patient and predict whether or not the patient has cancer. Or, for a less serious example, it can look at movies you’ve watched in the past and predict which new movies you’d like to watch now.
Quite often, machine learning is used for a task that doesn’t really sound like prediction, but it still is, in a way. For example, it could be used to look at a picture and say whether or not the picture contains a cat. That sounds more like identifying or classifying an object, but from the machine’s point of view, it’s a prediction because it doesn’t know for certain whether or not the picture contains a cat.
We’ll start with how to train a machine learning model. Then I’ll explain the concept of overfitting and some basic techniques for avoiding it.
Next, we’ll look at how to prepare your data if it’s not in a form that would work well with your machine learning model.
After that, I’ll show you how to deploy a trained model as a web service so that your applications can ask it to make predictions.
Finally, I’ll give you a brief overview of how to add custom code if you need something that ML Studio’s pre-built modules can’t provide.
By the end of this course, you should be able to prepare data for use by an Azure Machine Learning Studio experiment; train a machine learning model in Azure ML Studio; and deploy a trained model to make predictions based on new data.
We would love to get your feedback on this course, so please let us know what you think on the Comments tab below or by emailing email@example.com.
Now, if you’re ready to learn how to get the most out of Azure Machine Learning Studio, then let’s get started.
Free Azure trial: https://azure.microsoft.com/free
About the Author
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).