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Azure Machine Learning in Practice: From Fundamentals to Deployment

During this course, you will learn: Everything necessary to prepare your data, build, evaluate, and, most importantly, validate machine learning models, before deploying them to production, using the newest, 2020 version of Microsoft Azure Machine Learning.

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.

Target audience: 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.

Prerequisites: 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

Learning methods: 50% lectures, 30% demos, 20% tutorials.
Assesment methods: Execution of independent work.
Assesment form: Independent practical tasks on relevant topics.

More information: You can read more about the program here

Rafal Lukawiecki
He has been a popular speaker at major IT conferences since 1998, and he had the honour of sharing keynote platforms with Bill Gates and Neil Armstrong. A natural educator, he explains complex concepts in simple terms in his enjoyably energetic style

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Azure Machine Learning in Practice: From Fundamentals to Deployment