Fundamentals of AI and ML Competency (Intermediate Level)

  • 19m
  • 19 questions
The Fundamentals of AI and ML Competency (Intermediate Level) benchmark measures your knowledge of the key concepts and use cases of advanced data science, artificial intelligence (AI), and machine learning. You will be evaluated on your ability to recall ML methods and algorithms and outline strategies for each part of the AI life cycle. A learner who scores high on this benchmark demonstrates that they have good knowledge of the basics of AI and ML concepts.

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