• Online, Self-Paced

Feature engineering can be an essential tool in applied machine learning when enhancing a dataset. In this course, you will learn about concepts of feature engineering, including areas of streaming architecture and implementations.

Learning Objectives

GCP Feature Engineering

  • start the course
  • describe the concepts of feature engineering
  • recall the benefits of quality features with feature engineering and feature selection
  • describe the process of input selection in feature engineering
  • demonstrate feature engineering in use cases

Streaming Architecture

  • recall the concepts of streaming data and real-time stream processing
  • describe Dataflow triggers and late data
  • install Java JDK on Windows 10
  • demonstrate how to install Apache Maven on Windows 10
  • install Google Cloud SDK and initialize SDK Shell on Windows 10
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Java in Cloud SDK Shell
  • demonstrate the process of streaming pipelines using Dataflow SDK 2.x and Python in Google Cloud Shell

Practice: Feature Engineering and Streaming

  • describe feature engineering concepts and streaming data architecture

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture
  • Systems Development

Specialty Areas have been removed from the NICE Framework. With the recent release of the new NICE Framework data, updates to courses are underway. Until this course can be updated, this historical information is provided to give better context as to how it can help you with your cybersecurity goals.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.