Advanced masterclass 2 – Random forests: Melbourne, 10–11 September 2018
August 10 @ 9:30 am - August 11 @ 5:00 pm
This class will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R.
Access to these methods allows the user to easily solve problems not susceptible to other methods, including deep learning.
Topics will include:
- A brief overview of the random forest algorithm.
- Out-of-sample estimates on training data, and applications in fraud, risk and outlier detection—random forests can make confident predictions on training data, unlike most other methods.
- Single-model quantile regression—estimating a full distribution, not just the mean. Vital for risk-based estimation.
- The proximity matrix—a powerful visualisation, clustering and insights tool unique to random forests.
- Random forests as an unsupervised learning method—outlier detection and clustering when there are no target values—vital for fraud detection.
- ranger, a fast, flexible implementation of random forests in R.
- extraTrees (Extremely Randomized Trees), an extension to random forests that often adds more accuracy.
- Dealing with small data sets and small classes.
A range of other topics, including recent works and extensions of existing packages, may also be covered.
Trainees are expected to be familiar with R, the basics of machine learning and out-of-sample error estimation, and the basic workings of the random forest algorithm.
Earlybird pricing is available until 5 July 2018
Group bookings of two or more people attract discounts of up to 20% during the earlybird period: please contact us at firstname.lastname@example.org to take advantage of these special rates.
Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!
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For someone who does not come from an IT background R is a terrifying program. Before doing the Introduction to R course I had previously done other courses in R but always found myself in over my head because they assumed a high level of program experience (even course that required no prior programming knowledge). This course is not like that at all. It starts at ground zero and teaches you everything you need to know to be able to use R confidently in your everyday workplace. It is a must attend for anyone who wants use R!
Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha” moments.
It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.
—Charl Swart, Director of Business Operations, Unisys Credit Services
Questions and further information
Training for all courses will be conducted with Microsoft R Open, the Enhanced Distribution of R. “Predictive Analytics, Machine Learning and Data Science for Big Data” and related courses, along with all advanced courses, will also include use of Azure ML, Microsoft’s interactive machine learning platform in the cloud.
Meals and refreshments
Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.
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.
Cancellations and refunds
You can get a full refund if you cancel 2 weeks or more before the course starts. No refunds will be issued for cancellations made less than 2 weeks before the course starts.
Frequently asked questions (FAQ)
Do I need to bring my own computer?
There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.
I’m lost! How do I find the venue?
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