11 courses with the subject AI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
AI 101AI Literacy Electrocardiogram3
This course provides a comprehensive introduction to Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. Students explore neural networks, transformers, NLP concepts, and leading models such as BERT and GPT, with a focus on real-world business applications. The course includes hands-on practice with ChatGPT, prompt engineering, multimodal AI tools, and open-source GenAI platforms, while addressing ethical, security, and bias considerations. Learning is delivered online with practical exercises, assessments, and a final project to reinforce applied AI skills.
This course introduces fundamental programming concepts using Python. Students learn programming basics, data types, operators, control structures, functions, object-oriented programming, and threading. Through hands-on lab practice, self-learning resources, exercises, and a course-end project, students develop practical problem-solving and coding skills essential for further study in software development and AI-related technologies.
This course focuses on applied data science using Python. Students develop practical skills in data analysis, manipulation, visualization, and statistical modeling using tools such as NumPy and pandas. The course covers mathematical and statistical foundations, probability distributions, data wrangling, and feature engineering. Through hands-on labs, practice exercises, mentoring sessions, and a course-end project, students gain experience applying data science techniques to real-world datasets and problems. 105
This course focuses on applied data science using Python. Students develop practical skills in data analysis, manipulation, visualization, and statistical modeling using tools such as NumPy and pandas. The course covers mathematical and statistical foundations, probability distributions, data wrangling, and feature engineering. Through hands-on labs, practice exercises, mentoring sessions, and a course-end project, students gain experience applying data science techniques to real-world datasets and problems.
This course provides an in-depth study of deep learning concepts and techniques. Students explore deep neural networks, model optimization and performance improvement, convolutional and recurrent neural networks, transformer models for natural language processing, autoencoders, and transfer learning. The course also introduces object detection and unsupervised learning methods. Through hands-on lab practice, guided mentoring, and a comprehensive course-end project, students develop advanced skills to design, optimize, and apply deep learning solutions to real-world problems.
This course provides an in-depth exploration of Natural Language Processing (NLP) techniques using modern machine learning and deep learning approaches. Students study text vectorization, distributed representations, sequence and attention models, recurrent neural networks, and applications such as machine translation and document search. The course also introduces audio analytics and deep learning methods for speech processing. Through hands-on lab practice, guided mentoring sessions, and a comprehensive course-end project, students develop practical skills to design and implement NLP and speech-based solutions for real-world applications.
This course introduces the fundamentals of prompt engineering for Generative AI systems. Students learn how to effectively interact with ChatGPT by designing, refining, and optimizing prompts to achieve accurate and meaningful outputs. Through self-learning videos, practical exercises, and assessments, students develop essential skills for applying prompt engineering techniques in real-world academic and business scenarios.
This course introduces the use of Generative AI for automating workflows and improving productivity. Students learn foundational concepts of Generative AI–driven automation and explore how AI tools can be applied to streamline tasks and processes. Through self-learning videos, practice exercises, assessments, and mentoring sessions, students develop practical skills to design and implement basic AI- powered workflow automation solutions in academic and business environments. Cybersecurity (Degree) Associate of Applied Science Program Health, fire, and safety measures are emphasized in each course of the program. Courses may be administered via distance, hybrid, or in-person, on campus educational methods. Refer to Attendance for Distance Education or Hybrid Courses for attendance requirements.
