Who should attend
- Data scientists
- Functional managers
- Any professional that uses data to make business decisions
About the course
When faced with a large volume of unstructured data, the question quickly arises: what does this all mean? Techniques in machine learning offer the promise of a meaningful answer to that question. Unsupervised machine learning is a powerful tool that is being put to use in many disciplines. In this course, you’ll experience machine learning through scripting in the statistical programming language R.
The course focuses on using unsupervised machine learning to bring coherence to unstructured data. Specifically, you’ll use different methods to generate clusters within your data set when no dependent variable is specified. Using supervised machine learning approaches, you’ll build and evaluate models that allow you to classify your data and understand the marginal impacts of each attribute. And you’ll gain experience with powerful tools in R that allow you to efficiently evaluate competing models to find the one that gives you the most accurate results.
Participants who complete this course will be able to…
- Create clusters for structured data with no specified dependent variable
- Choose appropriate models and methods for classifying items in a set
- Apply and compare many solution methods simultaneously
Chris K Anderson is a Professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, he was on faculty at the Ivey School of Business in London, Ontario Canada. His main research focus is on revenue management and service pricing. He actively works with industry, acros...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.