Nov 2, 2016
Every day you see more and more examples of machine learning in your life. In this course, Getting Started with Azure Machine Learning, you will learn how to develop and deploy predictive solutions using Azure Machine Learning. First, you will see how, with a little dragging and dropping, you can create solutions from scratch. Next, if you already have a solution implemented in R or Python, you will learn how to scale them up with Azure Machine Learning. Finally, you’ll end the course by learning about how to maintain your Azure Machine Learning solution. After finishing this course, you’ll have gone from a machine learning novice to having a prediction solution service ready to integrate into your applications to make them smarter and more useful.
Section Introduction Transcripts
Hi, my name is Jerry Kurata. Welcome to my course, Getting Started with Azure Machine Learning. We see machine learning predictions being made every day. From helping doctors understand the probability a person has a disease, to determining whether a bank should give a person a loan. You may have wondered how these systems are designed, built, deployed, and maintained. This course will help you unravel that mystery as we use Azure Machine Learning to introduce you to machine learning and the technology behind it. You will see why companies are in such a rush to learn machine learning to grow their business and increase profits. You will learn how we can use Azure and Azure Machine Learning Studio to create machine learning predictive solutions, specifically, you will learn how to gather data, create machine-learning solutions that learn from that data, and evaluate their predictive power. Once we have our solution, we’ll deploy it via Azure. This will make our predictive solution available to users as a web service. And to ensure this service continues to provide great performance as data changes, we’ll go through the process of maintaining the solution. We will do much of our work with Azure Machine Learning Studio. Azure Machine Learning Studio lets us build much of our machine-learning solution by dragging and dropping modules onto a workspace, but it also lets us incorporate code written in R and Python into our solution. By the end of this course, you’ll know how to create, deploy, and maintain machine-learning solutions in Azure and make their predictive capabilities available to users worldwide. I look forward to you joining me in this journey of getting started with Azure Machine Learning from Pluralsight.