This course provides a comprehensive exploration of generative artificial intelligence (AI) and its practical applications in solving complex business challenges. This course combines theoretical instruction with hands-on experience, equipping participants with the skills to design, develop, and deploy Generative AI-driven solutions. Topics include prompt engineering, natural language processing (NLP), generative AI workflows, fine-tuning large language models (LLMs), agentic AI development, secure AI practices, and the evaluation of AI solutions. Participants will also gain insight into ethical AI practices to ensure responsible and fair use of this technology. The curriculum includes two hands-on projects and analysis of real-world case studies.This program is designed for technology professionals, data analysts, engineers, consultants, technical managers, and STEM graduates seeking to enhance their expertise in generative AI. A foundational understanding of programming and data analysis is recommended. Participants will develop proficiency in using tools such as Python, VS Code, and various libraries. They will also learn techniques for retrieval-augmented generation (RAG), quick fine-tuning with vector databases, and use of open-source LLMs for proprietary applications. This course prepares graduates to apply generative AI to a wide range of applications, develop and train generative models, apply these techniques to create content, evaluate ethical implications, and analyze the impact of AI on various industries and society.
Course modules typically run Monday through Sunday, with assignments due Sunday at midnight. Some variations occur so take careful note of due dates in Canvas.
This course aims to provide students with a foundational and applied understanding of generative artificial intelligence by exploring its core concepts, techniques, and real-world applications. Through hands-on activities, discussions, and project-based learning, students will gain the skills needed to design, build, and critically evaluate generative AI systems for practical use in diverse domains.
Not required
We recommend the use of VS Code as your Python editor.
You will require the latest version of Python and download open source packages as required.
Most modules will have a graded activity.
Quizzes (Modules 1 and 2) 10%
Assignments (Modules 3, 4, 5, 6, 9, 10, 11, 12) 40%
Mid-term Project (Module 8) 25%
Final Project (Module 14) 25%
EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).
| Score Range | Letter Grade |
|---|---|
| 100-97 | = A+ |
| 96-93 | = A |
| 92-90 | = A− |
| 89-87 | = B+ |
| 86-83 | = B |
| 82-80 | = B− |
| 79-77 | = C+ |
| 76-73 | = C |
| 72-70 | = C− |
| 69-67 | = D+ |
| 66-63 | = D |
| <63 | = F |
We are committed to providing an outstanding educational experience. We strive to make this course useful, fair, and exciting. We hope that we are approachable, available and that you can come to us with any concerns or ideas. We hope that we achieve the highest marks when you complete course evaluations, but if there is anything that would prevent you from giving us a 5 out of 5 rating, don't wait until the end-of-course evals. Talk to us immediately so that we can provide YOU the best experience possible.
Deadlines for Adding, Dropping, and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
Students with Disabilities - Accommodations and Accessibility
Student Conduct Code
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.