Course Outline


  • Overview of RAPIDS features and components
  • GPU computing concepts

Getting Started

  • Installing RAPIDS
  • cuDF, cUML, and Dask
  • Primitives, algorithms, and APIs

Managing and Training Data

  • Data preparation and ETL
  • Creating a training set using XGBoost
  • Testing the training model
  • Working with CuPy array
  • Using Apache Arrow data frames

Visualizing and Deploying Models

  • Graph analysis with cuGraph
  • Implementing Multi-GPU with Dask
  • Creating an interactive dashboard with cuXfilter
  • Inference and prediction examples


Summary and Next Steps


  • Familiarity with CUDA
  • Python programming experience


  • Data scientists
  • Developers
  14 Hours


Related Courses

Scaling Data Analysis with Python and Dask

  14 hours

Data Analysis with Python, Pandas, and Numpy

  14 hours

Accelerating Python Pandas Workflows with Modin

  14 hours

Machine Learning with Python and Pandas

  14 hours

FARM (FastAPI, React, and MongoDB) Full Stack Development

  14 hours

Developing APIs with Python and FastAPI

  14 hours

Web application development with Flask

  14 hours

Advanced Flask

  14 hours

Build REST APIs with Python and Flask

  14 hours

Kivy: Building Android Apps with Python

  7 hours

Game Development with PyGame

  7 hours

GUI Programming with Python and PyQt

  21 hours

Scientific Computing with Python SciPy

  7 hours

GUI Programming with Python and Tkinter

  14 hours

Web Development with Web2Py

  28 hours