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.

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