Introduction to H2O Driverless AI R Client

Introduction to H2O Driverless AI R Client

Course Level: Intermediate

Course Overview: H2O Driverless AI is a proprietary tool developed by to perform automatic machine learning without the need for coding. Automatic machine learning is the process of automating the tasks of applying machine learning to real-world problems. Driverless AI provides automatic feature engineering, model validation, model tuning, model selection and deployment and machine learning interpretability which can be used within an R environment. By the end of this one-day course you will be able to use Driverless AI to create and analyse machine learning models for your data within R.

Book: Introduction to H2O Driverless AI R Client

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Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • Introduction to H2O Driverless AI: A brief introduction to H2O Driverless AI including a quick overview on automated machine learning
  • Getting started with Driverless AI R Client: How to connect/create a Driverless AI instance
  • Loading and visualising data: How to create, load or view datasets; split into train/test/validation and visualise.
  • Launching an experiment: Discuss the different parameters that can be defined for an experiment for a regression or classification problem along with using these parameters when setting up and running an experiment.
  • Interpreting output: Explore feature engineering, summary information and model performance. Also download automated document report created using H2O AutoDoc.
  • Applying to new data: Apply a finalised model to new data (or test data) and assess fit.

Learning Outcomes

By the end of the course, participants will understand…

  • what is automated machine learning
  • how to create and connect to a Driverless AI instance
  • how to load a dataset into Driverless AI and set up an experiment
  • how to interpret model results and use the final model on new data.


Prior Knowledge

It will be assumed that participants are familiar with the R programming language. For example, familiarity with datasets, for loops and use of functions. Some exposure to common statistical terms would be an advantage, but not essential. Attending the Introduction to R and Programming with R courses will provide a sufficient background for this course.

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