After Completion
In general terms, after completing this course participants will be more familiar and confident with:
  • Common and most-used AI, Machine Learning, Deep Learning, and Data Science terminology.
  • What someone can expect realistically from these systems to do and what they actually cannot do.
  • Abilities to understand what the opportunities of an AI/ML product are, and how these can impact their institution, organization, or personal project.
  • Identify and prioritize the highest value applications for machine learning and do what it takes to make them successful.
  • Computational resources and recommended frameworks.
  • Best practices on data collection and curation.
  • Training, optimization, and monitoring of Machine Learning models.
  • Instructions on how to build reproducible machine learning pipelines.
  • Create continuous and automated integration to deploy models.
  • When building a product, what things we want the AI solution to improve or enhance when compared to traditional solutions.
  • Recommendations on how to deliver AI/ML-generated predictions to users.
  • Understand and practice with real-world cases and users' data.
Last modified 10mo ago
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