# 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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