Machine Learning and Data Science in R on Microsoft ML and SQL Servers

During this course, you will learn:

• Building and deploying machine learning models using open source R programming language, including data preparation, visualisation, and stringent model validation.
• High-performance ML using the newest version of Microsoft ML Server and SQL Server 2019 with R and RStudio.
• Deployment to production with nanosecond-scale performance.
• Successful data science project formulation and delivery.

Above all, this course will teach you modern R: currently, the most powerful language explicitly designed for advanced analytics, statistical learning, data science, and cutting-edge general-purpose machine learning. While Python is more popular as a universal programming language, also widely used for image and text analysis using deep learning, R is a clear leader in data science. You will learn how to do machine learning in R especially on classical data sets that you often encounter in business use. Even though such data might come from a data lake, typically you will find plenty of it in a data warehouse, a relational databases, or you can acquire it from transactional business application files, or from devices, such as: healthcare equipment, point-of-sales devices, or manufacturing and transportation machinery. Above all, R is great for exploratory analysis of data and it can help you draw meaningful conclusions from real-world experiments, such as A-B marketing tests or product trials. This course will teach you the foundations of hypothesis testing in order to be able to draw such conclusions with a high dose of confidence.

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

General ability to work with data in any form: using spreadsheets, tables, or databases. Prior knowledge of any programming language is helpful, however, if you are prepared to work harder by asking Rafal questions and doing a little additional homework during the week you can use this course to learn R as your very first programming language.

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