Fundamentals of AI and ML Competency (Intermediate Level)

Topics covered

• analyze the trade-offs between model complexity and interpretability
• define reinforcement learning, including its application in dynamic decision-making scenarios
• describe the process of and use cases for novelty detection
• identify AI tools and technologies
• identify common challenges faced during training and optimization of AI and machine learning (ML) models
• identify the types of data that are used with AI and evaluate the importance of data quality and quantity in successful AI implementations
• identify which methods to use and what questions to ask during the model, validate, and test stages
• list common machine learning challenges
• list considerations for evaluating the accuracy of a classification model
• list considerations for evaluating the accuracy of a clustering algorithm
• outline how to develop specific, measurable, and objective questions for your organization
• outline how to use multiple linear regression
• outline rule mining and its use cases
• outline strategies for evaluating the accuracy of text mining, as well as common text mining pitfalls
• outline the AI life cycle and its elements
• outline the potential of computer vision techniques in image recognition and object detection
• outline the process of feature engineering and its impact on model performance
• recognize how to get the right data for your AI project
• state the purpose of and use cases for graph analysis