Program profile · official sources
No Code and Agentic AI
No Code and Agentic AI is a 14-week online MIT Professional Education program, offered with Great Learning, that teaches AI and machine learning without writing code. Weekly units move from LLMs and prompt engineering through clustering, regression, classification and recommendation systems to RAG workflows, evaluating generative AI, single agents and multi-agent systems, with three project weeks and a learning break; four self-paced modules add deep learning, computer vision, responsible AI and time series. Mentors run small-group sessions. Completion earns a certificate of completion and 10 CEUs are listed. As shown in the United States on 4 October 2026, the course fee is USD 2,850 for the session dated Oct 10, 2026 to Feb 14, 2027.
Facts checked 4 Oct 2026
- Price
- USD 2850 (As shown in the United States on 4 October 2026, the official course page displays a Course Fee of $2,850 for the online session dated Oct 10, 2026 to Feb 14, 2027. Great Learning manages enrollments, including all payment services and invoicing. Taxes are not stated.)
- Time
- 14 weeks (Oct 10, 2026 - Feb 14, 2027 session listed)
- Level
- Not listed
- Credential
- Certificate of completion
Not stated on the captured page.
- Prerequisites
- No formal prerequisite is stated. The page says no-code platforms let professionals use these techniques without prior programming knowledge.
- Format
- Online, 14-week mentored program with recorded lectures, case studies, projects, quizzes, mentor-led sessions and webinars; offered with Great Learning
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Sources ↗
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See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Pre-Work: AI Foundations + No-Code Tool Setup & Onboarding
- Week 1: AI, Gen AI, and Agentic AI Landscape
- Week 2: LLMs and Prompt Engineering
- Week 3: Data Exploration
- Week 4: Prediction Methods - Regression
- Week 5: Prediction Methods - Decision Systems
- Week 6: Recommendation Systems
- Week 7: Project Week
- Week 8: Learning Break
- Week 9: Build Workflows on Proprietary Data and Business Context
- Week 10: Evaluating Generative AI Workflows
- Week 11: Project Week
- Week 12: Single Agent Systems
- Week 13: Build Autonomous Systems Using Multi-Agents
- Week 14: Project Week
- Deep Learning and Neural Networks
- Computer Vision Methods
- Ethical and Responsible AI
- Data Exploration: Temporal Data
Prerequisites: No formal prerequisite is stated. The page says no-code platforms let professionals use these techniques without prior programming knowledge.
What you actually do
- Pre-Work: AI Foundations + No-Code Tool Setup & Onboarding : Program week (provider curriculum) · Source
- Week 1: AI, Gen AI, and Agentic AI Landscape : Program week (provider curriculum) · Source
- Week 2: LLMs and Prompt Engineering : Program week (provider curriculum) · Source
- Week 3: Data Exploration : Program week (provider curriculum) · Source
- Week 4: Prediction Methods - Regression : Program week (provider curriculum) · Source
- Week 5: Prediction Methods - Decision Systems : Program week (provider curriculum) · Source
- Week 6: Recommendation Systems : Program week (provider curriculum) · Source
- Week 7: Project Week : Program week (provider curriculum) · Source
- Week 8: Learning Break : Program week (provider curriculum) · Source
- Week 9: Build Workflows on Proprietary Data and Business Context : Program week (provider curriculum) · Source
- Week 10: Evaluating Generative AI Workflows : Program week (provider curriculum) · Source
- Week 11: Project Week : Program week (provider curriculum) · Source
- Week 12: Single Agent Systems : Program week (provider curriculum) · Source
- Week 13: Build Autonomous Systems Using Multi-Agents : Program week (provider curriculum) · Source
- Week 14: Project Week : Program week (provider curriculum) · Source
- Deep Learning and Neural Networks : Self-paced module · Source
- Computer Vision Methods : Self-paced module · Source
- Ethical and Responsible AI : Self-paced module · Source
- Data Exploration: Temporal Data : Self-paced module · Source
What the credential is
- Credential type
- Certificate of completion · professional.mit.edu / no-code-and-agentic-ai
- Credential
- Certificate of completion from MIT Professional Education · professional.mit.edu / no-code-and-agentic-ai
- Issuer
- MIT Professional Education · professional.mit.edu / no-code-and-agentic-ai
- Sharing
- Not stated on the captured page. · professional.mit.edu / no-code-and-agentic-ai
- Exam requirement
- Successfully complete the program; the page lists hands-on projects and interactive quizzes but states no separate exam. · professional.mit.edu / no-code-and-agentic-ai
Total cost to finish
Estimate for the stated payment scope: USD 2850 × 1 = USD 2850.
- Course fee (one online session): USD 2850 × 1 = USD 2850. One enrollment in the listed session as displayed on the official page. · Source
Scope: Displayed course fee only; taxes, refunds and payment plans are not stated on the captured page. · Source
Alternatives in the registry
Related by topic or level.
- AI agent engineer : Check official price; 36 hours of content (provider-listed).
- Finetuning with Llama : Check official price; 2 hours (provider headline; section and exam times listed separately).
- Introduction to LLaMA : Check official price; 2 hours (provider headline; module and exam timings listed separately).
Change log
- : Initial record from the official course page captured from the United States on 4 October 2026. · Source