Course description

Candidates for this certification have a foundational knowledge the procedures used to develop an artificial intelligence (AI) solution, as well as an understanding of the issues surrounding the governance, transparency, security, and ethics of AI.  Successful candidates will be able to analyze and classify a problem. They should be able to demonstrate knowledge of data collection, data processing, and feature engineering strategies. Candidates should be able to choose an appropriate algorithm for training a model, and understand the metrics used to evaluate model performance. They should understand the AI development lifecycle and how a production pipeline is used to allow for continuous improvement.  Candidates at least 150 hours of instruction and/or exploration of artificial intelligence methodology and solutions.

What will I learn?

• AI Fundamentals & Theory: Understanding the history, objectives, and core concepts of AI, such as intelligent agents, knowledge representation, and reasoning.
• Algorithms & Problem Solving: Implementing search algorithms (e.g., A*, adversarial search) and applying logic-based planning to solve complex problems.
• Acquiring theoretical and technical knowledge in AI and machine learning
• Applying AI techniques to solve problems
• Designing and implementing AI models
• Analyzing AI system performance
• Developing strong professional and ethical skills

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