Learning AI no longer means choosing between a weekend introduction and a full-time return to university. Working professionals can now choose structured certificate programs that build applied skills within a few months or online master’s degrees that provide broader academic depth over a longer period.
The right choice depends on what needs to change professionally. Someone who wants to apply machine learning, GenAI, RAG, and AI agents in the workplace may prefer a concentrated professional program. A learner seeking graduate-level specialization, a formal degree, or deeper study across AI systems may find a master’s route more appropriate.
The five programs below show both paths, allowing professionals to compare learning commitment, technical depth, credential type, and cost.
5 AI Programs for Professional and Degree-Level Learning
|
# |
Program |
Provider |
Fees |
Eligibility |
Duration |
Credentials |
|
1 |
PG Program in Artificial Intelligence & Machine Learning |
Great Learning |
₹2,50,000 + GST |
Bachelor’s degree with 50% or equivalent |
23 weeks |
Post Graduate Certificate + 9 CEUs |
|
2 |
Professional Certificate in Machine Learning and Artificial Intelligence |
UC Berkeley Executive Education |
$7,975 |
Bachelor’s degree, strong math skills, some programming experience |
6 months |
Certificate of Completion |
|
3 |
Postgraduate Program in Artificial Intelligence and Machine Learning: Business Applications |
Texas McCombs |
$3,950 |
Bachelor’s degree with 50% or equivalent |
23 weeks |
Texas McCombs Post Graduate Certificate + 9 CEUs |
|
4 |
MS in Applied Artificial Intelligence |
University of San Diego |
$29,850 tuition |
Relevant bachelor’s degree; 2.75 GPA; math and programming preparation |
20 months |
Master of Science in Applied Artificial Intelligence |
|
5 |
MS in Applied Artificial Intelligence |
University of Oklahoma |
Approx. $30,450 |
Bachelor’s degree; minimum 2.5 GPA; programming and math foundation |
18+ months |
Master of Science in Applied Artificial Intelligence |
1. PG Program in Artificial Intelligence & Machine Learning – Great Learning
This AI and Machine Learning Course moves from Python and predictive modeling into deep learning, Generative AI, RAG, Agentic AI, and deployment. The curriculum is designed for professionals seeking substantial technical exposure without committing to a graduate degree.
Program Highlights: Python, scikit-learn, TensorFlow, neural networks, GenAI, RAG, LangChain, LangGraph, Hugging Face, Docker, Streamlit, 30+ tools, projects, and real-world case studies.
Duration: 23 weeks online with approximately 8 to 10 hours of weekly study.
Credentials: Post Graduate Certificate from Texas McCombs and 9 CEUs.
Outcomes: Learners build ML and deep learning models, RAG pipelines, single-agent and multi-agent workflows, and deployed AI applications.
Why Choose This Course?
- It condenses a broad modern AI stack into a professional learning format you can complete alongside full-time work.
- Applied work extends into GenAI and Agentic AI, rather than ending with traditional machine learning models.
2. Professional Certificate in Machine Learning and Artificial Intelligence – UC Berkeley Executive Education
UC Berkeley Executive Education offers another skill-acceleration route, but with stronger entry expectations in mathematics and programming. Learners progress through statistical foundations, machine learning techniques, neural networks, NLP, Generative AI, and applied projects.
Program Highlights: Python, data analytics, regression, classification, decision trees, neural networks, NLP, Generative AI, GitHub portfolio development, assignments, and a capstone.
Duration: Online, 6 months, with approximately 15 to 20 hours of weekly work.
Credentials: Certificate of Completion from UC Berkeley Executive Education.
Outcomes: Participants learn to select and build ML models, analyze real-world data, communicate results, and produce a professional project portfolio.
Why Choose This Course?
- The program suits professionals who already have some technical preparation and want a faster alternative to graduate study.
- Portfolio development is built into the experience, giving learners evidence of applied ML and AI work.
3. Post Graduate Program in Artificial Intelligence and Machine Learning: Business Applications – The McCombs School of Business at The University of Texas at Austin
This Artificial Intelligence Course combines technical AI development with business application. The learning journey includes Python, machine learning, deep learning, NLP, Generative AI, RAG, Agentic AI, case studies, and structured mentorship.
Program Highlights: 4 hands-on projects, 30+ case studies, 30+ tools, Python, ML, neural networks, RAG, GenAI, Agentic AI, mentoring, and an AI project portfolio.
Duration: 23 weeks online with an expected commitment of 8 to 10 hours per week.
Credentials: Post Graduate Certificate from Texas McCombs and 9 CEUs.
Outcomes: Learners develop AI applications, evaluate business use cases, automate workflows, and strengthen technical judgment around model performance and implementation.
Why Choose This Course?
- It provides university-backed professional education without the time commitment of a master’s degree.
- Business cases sit alongside technical projects and can help professionals connect AI development to organizational problems.
4. Master of Science in Applied Artificial Intelligence – University of San Diego
The University of San Diego provides a degree-level route for professionals seeking deeper technical study. The 30-unit curriculum covers machine learning, neural networks, NLP, computer vision, IoT, data systems, ethics, and applied AI development.
Program Highlights: 10 courses, machine learning, deep learning, NLP, computer vision, IoT, ethics, technical foundations, and a final capstone experience.
Duration: 100% online, 20 months over five semesters.
Credentials: Master of Science in Applied Artificial Intelligence.
Outcomes: Graduates develop and evaluate AI systems, apply AI techniques to industry problems, and address technical decisions alongside ethical and social considerations.
Why Choose This Course?
- It provides a formal graduate degree with substantially more academic commitment than a professional certificate.
- The curriculum combines technical AI development with responsible application and culminates in an applied capstone.
5. Master of Science in Applied Artificial Intelligence – University of Oklahoma
The University of Oklahoma’s 30-credit online degree is designed for professionals seeking advanced technical preparation in machine learning, deep learning, NLP, AI systems, and responsible AI.
Program Highlights: Machine learning, deep learning, NLP, AI system development, responsible AI, technical electives, model evaluation, and an applied practicum.
Duration: Fully online, 18+ months, with approximately 15 to 20 hours of weekly study.
Credentials: Master of Science in Applied Artificial Intelligence.
Outcomes: Learners develop, evaluate, and deploy AI solutions while building deeper expertise for AI engineering, machine learning, applied data science, and AI systems roles.
Why Choose This Course?
- The degree supports deeper technical specialization for professionals prepared for graduate-level programming and mathematics.
- Flexible electives and an applied practicum let learners connect academic study to a specific technical direction.
Conclusion
The decision between professional AI training and a master’s degree largely depends on depth, time, and credential goals. Certificate programs can help professionals apply current tools and techniques sooner, while graduate degrees provide a longer academic pathway with broader technical requirements.
When comparing AI courses, consider what you need from the credential as carefully as what you want to learn. Skill acceleration may make sense when immediate application is the priority, while a master’s degree may better align with long-term technical specialization and formal graduate education.
About the Author
Akriti Galav is a Content Manager at Great Learning. She is an alumna of the Institute of Rural Management, where she studied Management and Finance in depth. An avid researcher, she brings a strong analytical approach to her work and content. You can connect with her on LinkedIn.
