Azure Machine Learning in Practice: From Fundamentals to Deployment

We will use the brand-new Azure ML Designer UI, and the completely new Azure ML Studio (very different from the older ones) to teach all the fundamentals of machine learning. You will understand why and how to use specific algorithms, notably: classifiers such as Boosted Decision Trees, Logistics Regression and Neural Networks,  both linear and non-linea regressions, clustering, and recommenders. Though almost all of your work will be done using the graphical UI, you will also see how to code for Azure ML Service in Python and in a little R using the most popular Python libraries, such as scikit-learn. Although deep learning is not a focus for this course, you will also see how easy it can be to use it with Azure ML. If you already have some programming experience: that is great—but it is not necessary, as everything needed to use Azure ML, including every line of code, will be carefully explained during the course. If you are interested in learning R for more advanced ML and data science, please see our other course by Rafal that focuses on R and Microsoft ML and SQL Servers—which we do not cover in this course.

Analysts, budding and current data scientists, BI developers, programmers, power users, predictive modellers, forecasters, consultants, data engineers, anyone interested in using ML for AI, AI engineers.

There are no prerequisites other than general ability to work with data in any form: if you have used a spreadsheet, tables, databases, or you have written a program, no matter how long ago, you will be able to follow the course.

This course will teach you machine learning using Azure ML: you do not need to understand ML or data science before attending

50% lectures, 30% demos, 20% tutorials.

Execution of independent work.

You can read more about the program here