Advanced Machine Learning Masterclass: Auckland, 2–3 August 2018
August 2 @ 9:30 am - August 3 @ 5:00 pm
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.
Earlybird pricing is available until 19 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!
—Damon Rasheed, CEO, Rate Detective
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?
Please call +61 4 1457 3322 or email firstname.lastname@example.org if you can’t find the venue.
Presciient training, coaching, mentoring, and capability development for analytics
Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice, and other services to build your organisation’s strategic and operational analytics capability.
Our courses include:
- Introduction to R
- Predictive Modelling and Data Science for Big Data
- Forecasting and Trend Analysis
- Data Visualization
- Data Analytics for Fraud and Anomaly Detection in Forensics and Security
- Data Analytics for Campaign Marketing, Targeting and Insights
- Data Analytics for Insurance Claims analysis
- Data Analytics for Retail Marketing and Pricing
- Data Analytics for the Web
- Working with Data: Analysis and Report Writing for Everybody
By booking this course, you agree to our terms and conditions.
For any enquiries, please call +61 414 57 33 22.
If you prefer, you can pay by invoice rather than credit card. Just select “Pay by invoice” at the checkout.