Applied AI: Technical Skills & Development
This credential provides students with foundational and advanced artificial intelligence (AI) knowledge through two courses: ITD 1393 (Foundations of Artificial Intelligence) and ITD 2273 (Applied Artificial Intelligence & Advanced Techniques). The credential is designed to equip students with AI technical skills, enabling workforce entry in AI-related roles or serving as a stepping stone into the BT IT Artificial Intelligence Option.
- Type Validation
- Level Foundational
- Time Weeks
- Cost Paid
Skills
Earning Criteria
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ITD 1393 - Foundations of Artificial Intelligence (3 credit hours)
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ITD 2273 - Applied Artificial Intelligence & Advanced Techniques (3 credit hours)
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Skills covered: 1) Foundational Artificial Intelligence Concepts — Ability to explain core AI principles, terminology, history, and real-world applications across industries. 2) AI Prompting & Human-AI Interaction — Skill in effective AI prompting techniques, chatbot interaction, and applied AI communication strategies.
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Skills covered: 3) Skill covered: Python & AI Development Foundations — Competency in utilizing Python, Jupyter Notebooks, and foundational programming concepts used in AI systems. 4) Machine Learning & Predictive Analytics — Ability to implement supervised and unsupervised learning models for classification, regression, clustering, and recommendation systems.
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Skills covered: 5) Natural Language Processing & Speech Technologies — Proficiency in text analysis, sentiment analysis, tokenization, speech recognition, and conversational AI systems. 6) Computer Vision & Neural Networks — Application of object detection, image tracking, OCR systems, and neural network model development.
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Skills covered: 7) Problem Solving & Reinforcement Learning — Demonstrated ability to utilize heuristic search, logic programming, reinforcement learning, and genetic algorithms to solve technical problems. 8) Ethical & Responsible AI Implementation — Understanding of ethical AI considerations including privacy, bias, sustainability, fairness, and responsible data practices.
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Skills covered: 9) AI Workforce Readiness — Ability to evaluate emerging AI technologies and apply AI-supported technical skills within modern workforce environments.
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Learners will complete hands-on technical labs, coding projects, machine learning activities, AI implementation exercises, and applied problem-solving assignments in both courses. Students will develop portfolio-ready AI artifacts including predictive models, recommender systems, chatbot applications, and computer vision demonstrations as evidence of competency mastery. Students must successfully complete both courses earning a grade of C or higher.