Artificial Intelligence Associate

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The CIW Artificial Intelligence Associate certification is the part of the CIW Artificial Intelligence series which provides a broad understanding into the world of AI careers. This exam validates in-depth knowledge of AI (history, definition, methods and algorithms, applications, careers, etc.), privacy concerns, ethical issues in AI, and responsible development; an understanding of the essentials of AI system design, program design structure, problem identification, and solution implementation; and a working knowledge of application deployment, testing, data management (dataset creation, selection and curation).

The CIW Artificial Intelligence Associate course* prepares candidates to take the CIW Artificial Intelligence Associate exam, which, if passed, earns the individual the CIW Artificial Intelligence Associate certification.

*The courseware is not required to sit for the certification exam.

Target Audience

  • AI Research
  • Software Engineers
  • UI/UX Developers
  • Data Analytics
  • Big Data Engineers/Architects
  • Machine Learning Engineers
  • Business Intelligence Developers

Skills Assessed

  • Basic knowledge of AI machine learning, deep learning and computer science
  • How to solve problems using AI
  • How to apply reasoning to deal with contingencies while planning
  • How logic is used to build reasoning
  • How to identify compromised information, misinformation and deepfakes.
  • What social aspects of AI impact communities and AI impact on productivity
  • How AI can improve user experience
  • AI system design and other factors affecting the cost of developing ML models
  • Selecting, curating, and creating datasets for cleaning, sampling, storage and other tasks
  • Knowledge of how algorithms used in AI, and how to distinguish deep learning from other learning algorithms.
  • Managing legal, ethical and privacy issues within AI systems design, development and deployment
  • Basic Machine Learning models and how predictions and decisions are made
  • Statistical concepts and foundations, including descriptive statistics, testing concepts and methods
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