Artificial Intelligence Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will further improve proficiency in AI systems, neural network architectures and related learning and functional practices and patterns, as they are applied and combined to solve a series of real-world problems.

For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. For individual completion of this course as part of a study kit, a number of supplements are provided to help participants carry out exercises with guidance.

Key Outcomes:
Students will be able to know:

• Types, approaches, in the industry of AI
• Common AI System Design Principles
• Common AI Project Best Practices
• Feature Encoding, Feature Imputation, Feature Scaling, Text Representation, Dimensionality Reduction of Data Wrangling Patterns
• etc