Data Preparation for AI

Current Status
Not Enrolled
Price
Included in Subscription
Get Started

“Garbage in is garbage out” is very much the case when it comes to systems that rely on data to learn, and AI systems are certainly no exception. Without clean and accurate data you run the risk of training your AI systems on bad data, resulting in skewed or inaccurate results. In this course we’ll go over best practices for preparing data for use with AI. This intermediate level course is appropriate for project managers, data scientists, or those in a more technical role.

What Will I Learn?

  • The need for good data
  • The role of data engineering
  • Data engineering in the context of AI
  • Data Collection & Sourcing
  • Data Selection
  • Data Cleansing
  • Data Enhancement & Augmentation
  • Data Labeling
  • Data Splitting
  • Data Formatting
  • Data Pipelines
  • Supporting Model Training Needs

Who is this Training For?

  • Project Manager
  • Product Manager
  • Capability Manager
  • Technical Manager
  • Acquisitions Manager
  • Procurement & Contracting Officers
  • Computer Systems Programmer
  • Developer
  • Artificial Intelligence Research Associate
  • Test & Evaluation Engineer,
  • System Engineer
  • Network Analyst
  • Data Analyst
  • Operations Research Analyst
  • Data Scientist
  • AI / Machine Learning (ML) Engineer
  • AI Assurance Engineer
  • Deployment Engineer
  • Knowledge Operations Manager
  • Network Infrastructure Engineer
  • Information Technician
  • Data engineer
  • Network operations
  • Information technician
  • Data technician
  • Program manager
  • Project manager
  • Supply program manager
  • Designers

Supported Learning Paths

  • Cognilytica: CPMAI Certification
  • DoD JAIC: Drive AI, Create AI, Embed AI, Facilitate AI
  • Edison DSF: Data Science Professionals (DSP04 -DSP09), Data Engineering & Management Professionals (DSP10-DSP16), Data & User Support (DSP20-DSP22)

Learning Levels

  • DoD JAIC AI: Intermediate (Applied) Level
  • Edison DSF: Level 4-5
  • Category: Data Engineering & Management