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.