Learning Outcomes: 

After joining this course, participants will understand how to:

•        Identify the problem we are trying to solve using AI (e.g., user segmentation, improving customer service)
•        Classify the problem (e.g., regression, unsupervised learning)
•        Identify the areas of expertise needed to solve the problem.
•        Build a security plan.
•        Ensure that AI is used appropriately.
•        Choose transparency and validation activities.
•        Assess data quality.
•        Ensure that data are representative.
•        Select features for the AI model.
•        Identify training and test data sets.
•        Consider the applicability of specific algorithms.
•        Train a model using the selected algorithm.
•        Evaluate model performance (e.g., accuracy, precision)
•        Look for potential sources of bias in the algorithm.
•        Obtain stakeholder approval.
•        Plan to address potential challenges of models in production.
•        Design a production pipeline, including application integration.
•        Support the AI solution.
•        Assess business impact (Key performance indicators)
•        Measure impacts on individuals and communities.
•        Consider improvement or decommission on a regular basis.

 Investment:

Curricula:

•        AI Problem Definition
•        Data Collection, Processing, and Engineering
•        AI Algorithms and Models
•        Application Integration and Deployment
•        Maintaining and Monitoring AI in Production