Introduction to Automatic and Scalable Machine Learning with H2O and R (intermediate)
Time and place
January 10th 2019, 09:00-16:00, Niels Henrik Abels hus, room 126
Choosing best model for your machine learning problem may be daunting task. H2O AutoML simplifies the process of setting up the complex machine learning modeling pipelines to just a few lines of code. You will learn how to make your own stacked ensemble in the first 30 min of the workshop. For those who want to stick around for the rest of the day, we will go a bit deeper into specification of individual models (GLM, GBM, Neural Networks, etc) and understanding of model performance.
The optional afternoon session will be dedicated to scalable data wrangling with data.table package in R.
Please, note that this is intermediate (advanced) workshop. Prerequisite knowledge: 2-day tidyverse workshop or equivalent proficiency in base R (visualization, data wrangling, functions). Experience with setting up and evaluating Machine Learning models.
This workshop assumes familiarity with principles of machine learning. Experience with running regression and classification models in R, including linear models and tree-based models. Experience with data manipulation packages (dplyr or data.table).
A laptop with Java Runtime installed in addition to R and Rstudio is required
Workshop will be useful for researchers and practitioners interested in setting up robust and scalable Machine Learning pipelines and making predictions on tabular data. We expect participants to be well versed in data wrangling and familiar with principles of setting up and evaluating Machine Learning models.
Dmytro Perepolkin (DataGym) and Raoul Wolf (NIVA)