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.