This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers.
Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.
Topics covered will include data exploration, data preparation, feature engineering, and prediction, using advanced modelling techniques including glmnet, xgboost, and random forests. Participants will also use unsupervised learning techniques to explore data and build features.
The course will also cover advanced model-evaluation techniques such as n-fold cross validation, model ensembling techniques, and advanced tips and tricks.
This course is suitable for trainees who have attended the courses “Introduction to R and Data Visualisation” and “Predictive Analytics and Data Science”, or for those with some knowledge of machine learning and R.
Course content may vary from this outline, and will be driven by class learning needs. This course may also be offered under other names.
The Introduction to R course provided clear and logical assistance to getting up and running with R. More than that, the real value was in providing guidance on the myriad of online resources and introducing me to a network of passionate and helpful R users. Eugene is a knowledgeable and approachable teacher. I wouldn’t hesitate in recommending the course. I feel that I am now fully on the road to applying R and using data to improve efficiency across my organisation.
—James Orton, Data and IT Manager, UNICEF Australia
I have been trying to convert my Stata programming skills to R, however, there have been many times where I just wanted to sit down with someone and have them explain the fundamentals of programming in R. Sure, a number of books and websites have helped me become familiar with R, however, I still didn’t feel ready to translate all of my familiar Stata commands to R (e.g. I am comfortable plotting graphics using ggplot2, however, revert back to Stata for data manipulation). I knew that a more effective way to learn and feel confident would be to sit down with someone and have them explain how they use R, how they clean data, how they plot graphics, etc. I knew that once I felt comfortable with cleaning my data in R, analysis would be less of an issue— I’m happy to research the specifics on my own.
Thank you Eugene for advancing my R skills. I especially appreciate the time spent explaining the fundamentals of data manipulation — i.e. the code one needs to know before running any basic or sophisticated analysis. The pace of the workshop was perfect.
—Dr Chelsea Wise, Lecturer, Marketing, UTS Business School
The course assumes no tertiary level training in statistics. Attendees simply need to be familiar with working with structured, electronic data.
Please ask about our discounts for group bookings.
Use email@example.com to email us any questions about the course, including requests for more detail, specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Course material may vary from what is advertised due to the demands and learning pace of attendees. Additional material may be presented along with or in place of what is advertised.
The course may be cancelled by the organisers with full refund of fees up to a week in advance of the scheduled commencement date.