Build AI Agents and Intelligent Workflows Without Coding

No Code and Agentic AI

Application closes on 8th Oct 2026

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No Code and Agentic AI program

Application closes 8th Oct 2026

Why should you join this program?

No Code Program to Build AI Agents & Intelligent Workflows

Learn from MIT Faculty through recorded video lectures & build practical expertise in No Code Agentic AI through 14+ real-world case studies, 3 hands-on projects using no-code tools.
Built on MIT’s Legacy of Innovation

MIT is ranked #1 in the world, #1 in AI and Data Science, and #2 among U.S. national universities, reflecting its global leadership in research, innovation, and technology education. (2026 Rankings)

PROGRAM OUTCOMES

What will you learn to build and apply?

Through a structured learning journey, you will build the capability to:

Build autonomous agents capable of planning, memory, tool use, and executing multi-step tasks.
Design systems where multiple AI agents collaborate on complex tasks and measure performance.
Transform data into actionable insights using intuitive, no code platforms.
Rapidly prototype, test, and operationalize machine learning models without writing code.
Leverage supervised and unsupervised learning, recommendation systems, deep learning, and computer vision.
Utilize Generative AI, Prompt Engineering, and Agentic AI to design intelligent, autonomous workflows.

KEY PROGRAM HIGHLIGHTS

Why choose this program?

Learn from MIT faculty

Build practical expertise in No-Code Agentic AI & GenAI through recorded sessions by MIT Faculty and apply concepts through domain-specific case studies & business applications.
Attend Mentorship Sessions by Industry Experts

Learn from experienced industry practitioners who help connect concepts, tools, and frameworks to real-world business applications.
Innovate Using No-Code Tools

Gain practical experience using tools such as KNIME, n8n, Google AI Studio, NotebookLM, and Claude to build AI-driven solutions.
Build AI Agents and Intelligent Workflows

Work on hands-on projects and case studies to design AI agents, intelligent workflows, and AI-powered solutions for business challenges.
Personalized Learning Support

Receive guidance from a dedicated program support team at Great Learning that supports your learning journey and helps you stay on track toward program completion.
Earn a Recognized MIT Professional Education Credential

Earn a Certificate of Completion and 10 Continuing Education Units (CEUs) from MIT Professional Education upon successful completion of the program.

Skills you will learn

Artificial Intelligence

Machine Learning

Deep Learning

Prompt Engineering

Generative AI

Agentic AI

Retrieval-Augmented Generation (RAG)

Computer Vision

Supervised and Unsupervised Learning

Model Evaluation & Tuning

Recommendation Systems

KNIME Workflows

Clustering Classification and Regression

Ethical and Responsible ai

LLM Integration

Artificial Intelligence

Machine Learning

Deep Learning

Prompt Engineering

Generative AI

Agentic AI

Retrieval-Augmented Generation (RAG)

Computer Vision

Supervised and Unsupervised Learning

Model Evaluation & Tuning

Recommendation Systems

KNIME Workflows

Clustering Classification and Regression

Ethical and Responsible ai

LLM Integration

Who is the program for?

Professionals from technical and non-technical backgrounds ready to advance their skills in AI

Business Leaders and Functional Heads

Seeking to lead AI initiatives and guide their teams.
Professionals in Tech-Adjacent Roles

Including business analysts and product managers looking to create rapid AI prototypes and build intelligent workflows.
Functional Managers

Across marketing, operations, legal, and finance looking to understand AI applications, boost productivity, and design intelligent workflows.
Entrepreneurs and Independent Consultants

Aiming to innovate, drive growth, and build practical AI solutions.

How's the learning experience of the program?

Build strategic judgement and human intuition with our unique structured learning approach.

Learn from Experts

Learn from MIT faculty and industry experts to build practical expertise in Agentic and Gen AI
Learn By Doing

Work on business problems using tools & build an e-portfolio of AI projects
Earn a University Credential

Earn a certificate of completion and 10 Continuing Education Units (CEUs) from MIT PE
Get Support Throughout the Learning Journey

A program support team will help you stay on track, navigate key milestones

What will you learn in the program?

The industry-relevant curriculum includes modules covering Generative AI concepts on Prompt Engineering, Retrieval Augmented Generation (RAG), and Agentic AI, equipping professionals to apply AI solutions using intuitive no-code tools.

Pre-Work

Establish a foundational understanding of data-driven decision-making and gain hands-on experience with no-code tools before the program begins.

Concepts Covered

- Origins of data-driven decision-making - Paradigms of Data Science and AI - Role of mathematics and statistics in AI/DS - Environment setup for KNIME and n8n - Navigating interfaces and core functionalities - Building and executing your first AI workflow

Week 1: AI, Gen AI, and Agentic AI Landscape

Understand the full arc of AI evolution and contextualize where Generative and Agentic AI fit within the broader landscape.

Concepts Covered

- AI evolution & architectural shifts: rule-based systems → ML → deep learning → transformers → generative AI → agentic systems - Key architectural breakthroughs driving each transition - The evolution and challenges of data operations - Use cases and practical applications of data operations

Week 2: LLMs and Prompt Engineering

Understand how large language models work and apply prompt engineering techniques to produce reliable, high-quality outputs.

Concepts Covered

- Evolution of generative models - Mechanics of diffusion models and LLMs - Challenges, hallucinations, and alignment - Common use cases: chatbots, content generation, summarization - Impact on decision-making: speed, scalability, limitations -Training process of foundation models and in-context learning - Prompt engineering techniques for improving output quality and consistency

Week 3: Data Exploration

Apply clustering and dimensionality reduction techniques to segment data and extract meaningful patterns.

Concepts Covered

- K-means clustering - K-medoids clustering - Gaussian mixture models (GMM) - Applying clustering for data segmentation and pattern extraction - Principal component analysis (PCA) - t-SNE for visualization - Transforming high-dimensional data into interpretable representations

Week 4: Prediction Methods — Regression

Build and evaluate regression models using no-code tools to predict numerical outcomes and identify key drivers.

Concepts Covered

- Fundamentals of supervised learning - Linear regression for predicting numerical outcomes - Interpreting model outputs to identify key drivers - Using KNIME for regression workflows - Testing basic statistical assumptions - Applying performance metrics for model evaluation

Week 5: Prediction Methods — Decision Systems

Apply classification techniques and ensemble methods to real-world categorization problems, including text classification using LLMs.

Concepts Covered

- Fundamentals of classification in supervised learning - Decision trees for categorization and prediction tasks - Classification performance metrics - Improving performance using ensemble methods - Random forest for enhanced classification - Using LLMs for text classification tasks - Enhancing classification with generative AI techniques

Week 6: Recommendation Systems

Build and apply recommendation systems using rank-based, content-based, and collaborative filtering approaches.

Concepts Covered

- Common recommendation patterns in everyday applications - How recommenders drive user experience - Rank-based recommendation methods - Content-based filtering - Collaborative filtering approaches - Applying recommendation techniques to real-world data

Week 7: Project Week

Predict which hotel bookings are likely to be cancelled to reduce revenue loss and support the design of more effective cancellation policies for a hotel group.

Week 8: Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Week 9: Build Workflows on Proprietary Data and Business Context

Build and evaluate RAG pipelines that connect LLMs to external knowledge sources for more reliable, grounded outputs.

Concepts Covered

- Attention mechanism fundamentals - Variants: masking techniques and multi-head attention - Role of positional encoding in sequence understanding - Vision transformers (ViT) for image-based tasks - Role of external knowledge sources in improving accuracy and reliability - Data chunking techniques - Embeddings for representing unstructured data - Building RAG pipelines - Evaluating RAG for accuracy and performance improvements

Week 10: Evaluating Generative AI Workflows

Apply structured evaluation methods to assess generative AI outputs and optimize prompts for reliability and accuracy.

Concepts Covered

- Metrics for text evaluation: ROUGE, BERTScore - LLM-as-a-judge for objective assessment - Identifying hallucinations through consistency checks - Prompt optimization techniques for better accuracy and reliability

Week 11: Project Week

Help financial analysts extract key information from lengthy annual reports to improve decision-making efficiency.

Week 12: Single Agent Systems

Design and deploy single AI agents that can plan, remember, use tools, and complete multi-step business tasks autonomously.

Concepts Covered

- Agent-environment interaction framework - Core elements: states, actions, rewards, policy - Q-learning: value-based learning for decision-making - Policy gradient methods: direct policy optimization - Transition from reactive LLMs to autonomous agents - Key characteristics and use cases of AI agents - Memory, planning, and tool usage in agent architectures - Designing task-oriented agent workflows - Applying agents to solve specific business problems

Week 13: Build Autonomous Systems Using Multi-Agents

Design and evaluate multi-agent systems where agents collaborate, hand off tasks, and handle real-world complexity.

Concepts Covered

- Designing collaborative agent systems - Dynamic task routing across agents - Handling uncertainty and errors in agent workflows - Constructing workflows using multi-agent collaboration - Applying adaptive RAG in generative AI systems - Defining evaluation metrics (e.g., tool accuracy) - Measuring effectiveness of agent-based systems

Week 14: Project Week

Improve support efficiency by implementing an agentic AI system that classifies tickets, retrieves knowledge, generates policy-compliant responses, and handles escalation.

Self-Paced Modules

Note: Weeks are indicative and subject to vary as per holiday schedule for the cohort

Deep Learning and Neural Networks

Introduces the fundamentals of deep learning, covering the building blocks of neural networks, how they are structured, and how they learn from data. Learners will explore key concepts such as layers, activation functions, and training processes, before applying these ideas to practical tasks like digit recognition. By the end of the module, learners will have a foundational understanding of how neural networks are designed, trained, and used in real-world AI applications.

Computer Vision Methods

Explores how AI systems interpret and analyze visual data, beginning with the limitations of traditional artificial neural networks in image-based tasks. Learners will then dive into the building blocks of convolutional neural networks, understanding how they are designed to capture spatial patterns and features in images. The module covers how these models are trained and optimized, culminating in practical applications such as image detection. By the end, learners will understand how modern computer vision systems are built and applied in real-world scenarios.

Ethical and Responsible AI

Introduces the principles of building AI systems that are ethical, transparent, and accountable. Learners will explore the AI lifecycle and examine how bias can arise at different stages, along with real-world examples. The module also covers key concepts such as causality, privacy, and the broader interconnections across domains that influence AI outcomes. By understanding interdependencies and feedback loops within AI systems, learners will gain the ability to critically evaluate and design AI solutions that are responsible and trustworthy.

Data Exploration: Temporal Data

Introduces time series as a unique data modality that requires specialized techniques for analysis. Learners will understand the key components of time series data, including trend, seasonality, and noise, and how to identify and estimate these patterns. The module also covers foundational methods for time series forecasting, enabling learners to analyze temporal data and generate informed predictions for real-world applications.

Case Studies

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

AI-Powered Chatbot to Handle Retail Order Queries

RETAIL Understand how an AI-powered chatbot enables dynamic, context-aware interactions to assist with product queries and order tracking, improving the overall shopping experience. Key Skills You Will Learn: Prompt Engineering, LLMs, AI Agents

Product Feasibility Intelligence

HEALTHCARE Analyze the feasibility of a new medical device by reviewing process documentation, comparing requirements, and generating a structured feasibility assessment. Key Skills You Will Learn: Document Analysis, LLMs, RAG

Global Socio-Economic Segmentation

PUBLIC POLICY Analyze country-level socio-economic data to segment nations into meaningful development groups such as underdeveloped, developing, and developed economies. Key Skills You Will Learn: EDA, K-Means Clustering, K-Medoids Clustering

Streaming Viewership Analysis

MEDIA & ENTERTAINMENT Analyze key drivers of first-day content viewership to support data-driven improvements in marketing, scheduling, and release strategies. Key Skills You Will Learn: EDA, Linear Regression

Product Sentiment Intelligence

CONSUMER TECH Analyze large volumes of smartwatch reviews across social and retail platforms to quantify overall sentiment and identify key product pain points. Key Skills You Will Learn: Sentiment Analysis, Aspect-Based Analysis, LLMs

E-Commerce Recommendation Engine

E-COMMERCE Generate personalized recommendations by combining purchase history, cart data, and real-time clicks to improve conversion rates. Key Skills You Will Learn: Collaborative Filtering, Content-Based Filtering

Health Insurance Assistant

INSURANCE Build a RAG-based chatbot that delivers accurate, context-aware responses to health insurance policy queries using external knowledge sources. Key Skills You Will Learn: RAG, Knowledge Retrieval, LLMs

Investment Advisory Assistant

FINANCE Improve the reliability of an AI assistant handling investment and compliance data through consistency checks and prompt optimization within a RAG pipeline. Key Skills You Will Learn: RAG, Consistency Checks, Prompt Optimization

Autonomous Inventory Replenishment Agent

SUPPLY CHAIN Develop an AI agent that monitors stock levels, forecasts demand, identifies optimal suppliers, and automates purchase order creation to streamline inventory replenishment. Key Skills You Will Learn: AI Agents, Demand Forecasting, Agentic Workflows

Regulatory Intelligence Assistant

LEGAL Build an AI-powered assistant that retrieves regulatory data, synthesizes insights from multiple sources, and generates structured compliance summaries. Key Skills You Will Learn: Agentic Workflow, RAG, LLM-Based Evaluation

Note: The curriculum listed above is indicative and subject to updates as technology evolves.

What case studies & projects will you solve?

Work on real-world case studies across industries & functions using no-code tools and technologies.

3

hands-on projects
14+

case studies

Hospitality

Automated Booking Cancellation Prediction

Description

Predict which hotel bookings are likely to be cancelled to reduce revenue loss and support the design of more effective cancellation policies for a hotel group.

Skills you will learn

Clustering
Classification
Regression
KNIME Workflows
Model Evaluation

Finance

Financial Report Analyzer

Description

Focuses on using Generative AI tools to automate the creation of presentation scripts, specifically for the topic “AI: Revolutionizing Modern Marketing.” It tackles the challenge of transforming complex marketing insights into concise, engaging content that effectively communicates key messages within time constraints.

Skills you will learn

RAG Pipelines
Embeddings
Data Chunking
LLM Integration

Customer Support

AI Helpdesk — Agentic Customer Support System

Description

Improve support efficiency by implementing an agentic AI system that classifies tickets, retrieves knowledge, generates policy-compliant responses, and handles escalation.

Skills you will learn

AI Agents
RAG
Multi-Agent Design
Tool Usage
Output Evaluation

Note: The projects listed above are indicative and subject to updates to the curriculum.

Which tools will you learn and apply?

Learn tools like KNIME, n8n, OpenAI, Claude & more to build, evaluate, and deploy intelligent AI systems.

KNIME
n8n
Google AI Studio
Gemini Notebook
Claude

Note: The tools listed above are indicative and subject to updates as technology evolves.

Earn a Certificate of Completion from MIT Professional Education

Certificate of Completion from MIT Professional Education upon successful completion of the program

* Image for illustration only. Certificate subject to change.

Who are the faculty for the program?

Learn from renowned MIT faculty and build technical intuition to make credible, strategic decisions.

Munther Dahleh

William A. Coolidge Professor, EECS and IDSS; Founding Director, IDSS

Trailblazer in robust control and computational design.

Director propelling interdisciplinary research and innovation.

Know More
Stefanie Jegelka

Associate Professor, EECS and IDSS

Expert in algorithms and optimization for AI.

Pioneer advancing theoretical machine learning foundations.

Know More
Devavrat Shah

Andrew (1956) and Erna Viterbi Professor, EECS and IDSS

Renowned expert in large-scale network inference.

Award-winning innovator in data-driven decisions.

Know More
John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Leader in optimization, control, and learning.

Renowned scholar with multiple prestigious accolades.

Know More
Caroline Uhler

Professor, EECS and IDSS

Expert in computational biology, statistics, and systems.

Award-winning scholar relentlessly driving transformative data insights.

Know More

Who are the mentors for weekly live sessions?

Learn from seasoned AI industry mentors to apply concepts and build practical skills.

Cristiano Santos De Aguiar

Biomedical Machine Learning Engineer, Oncoustics
Peyman Hessari

Senior Data Scientist, ATB Financial
Jatin Dawar

Senior Machine Learning Engineer, Telus
Olabode James

Machine Learning Architect, Rubik Technologies

Note: The mentors listed above are indicative and subject to change based on availability and scheduling.

What are the reviews for the program?

Learners share their feedback on the faculty, mentors, hands-on projects, and overall learning experience.

5.0

Bridge the gap between AI and industrial applications

I highly recommend this program to any professional looking to bridge the gap between AI and industrial applications.

Read More

David Nambete Luyadisa

SAIPEM - SAILUX

Energy, Automobile & Manufacturing >15 yrs

Oct 2026
5.0

Easy-to-understand interface and easy access to courses

Easy-to-understand interface and easy access to courses. The only issue I noticed was that, the first few times, my preferences had to be set up each time I logged in. I also found the notifications and alerts about upcoming activities and sessions helpful.

Read More

Paul Encalada

JDE Analyst, Andes Petroleum

Energy, Automobile & Manufacturing 12-15 yrs

Oct 2026
5.0

I use this to extend our own software products with AI capabilities

The best part of Great Learning was the combination of theory and practice. The statistics deep dive teaches just as much as you need to understand AI, and it works even for non-statisticians. The hands-on work with KNIME and n8n is highly practical, and the mentor sessions were pure gold - that’s where the real learning happened. There was very little to dislike. At the start, one mentor wasn’t a good fit for me, but I was able to switch groups quickly. A few instructors were harder to follow at times, but neither issue affected the overall value of the program. I learned how to prepare and process data with AI agents for maximum value at minimal cost, and how to make AI-generated results deterministic. As CEO of Change Factory AG, I use this to extend our own software products with AI capabilities in a reliable, cost-efficient way, even without a data science background.

Read More

Mike Portworsnick

Change Factory

IT, Technology & Telecom >15 yrs

Oct 2026
5.0

The program is very well structured for understanding AI without deep coding knowledge.

I recently completed the MIT No-Code AI and Machine Learning program, and it was a highly valuable learning experience. The program is very well structured, especially for those who want to understand AI and Machine Learning concepts without deep coding knowledge. The content is practical, relevant, and easy to follow, making complex topics much more approachable.

Read More

Muhammad Shakir Fattani

Finance, Insurance & Banking Services 5-8 yrs

Apr 2026
4.0

The MIT No-Code AI program gave me the confidence to apply AI in real-world solutions.

I developed a strong interest in AI at a point when I was rethinking my career direction. After years in corporate leadership roles, I wanted to move towards something more future-focused, where I could combine strategy, innovation, and automation to create meaningful impact. With that in mind, I enrolled in the MIT No-Code AI and Machine Learning program. The program gave me a solid foundation in how AI actually works. More importantly, it helped me think in systems—understanding data, model limitations, and how to translate business problems into practical AI-driven solutions. This perspective has been especially valuable in navigating today’s business landscape. This learning experience accelerated my transition into AI consulting. I began designing and applying real-world solutions such as AI workflow audits, internal knowledge assistants, and automation pipelines using tools like n8n. Instead of just learning theory, I was able to bring these concepts into practical use, particularly in customer service and operations. I am really glad that I took this step. The program did not just teach me AI, it gave me the confidence and structure to start building something of my own.

Read More

Randy Chia

Consulting & HR >15 yrs

Apr 2026
4.0

No-Code AI" will significantly impact future companies, reducing effort for decision-making and automation.

I believe that “No-Code AI” will have a major impact on the companies of the future. The effort required for data-driven decision-making and process automation will be significantly reduced. This was the reason why the concept of the program immediately convinced me. The lectures are challenging but very well structured from a didactic perspective. If there are difficulties in understanding, mentors help during the learning sessions. The fundamentals of AI algorithms are taught very well. The initial focus is on deterministic methods and their application to decision-making processes. In case studies, I learned the end-to-end process from project goal definition, data preprocessing, data exploration, feature engineering, setting up ML models, parameter optimization, and interpretation of results. With the help of RapidMiner and KNIME, I solved projects independently, supported by hands-on sessions. As the program progresses, I learned how neural networks work and how they are applied to decision and classification problems. The focus on black-box models highlighted the importance of explainability in AI systems. It would have been helpful to explore more approaches for interpreting AI decisions, as this is important for organizational adoption. At the end, the focus shifted to generative AI, covering transformer and diffusion models along with prompt engineering. We also explored tools like n8n for automation and agentic workflows. Overall, this experience combined strong academic content with practical guidance, helping me build skills that I can apply in business contexts such as sales, marketing, and AI adoption in organizations.

Read More

Wenzel Butting

Retail, Marketing & Advertising >15 yrs

Mar 2026
4.0

Advanced AI concepts made accessible with high-quality video lectures and live mentored sessions.

Wonderful learning experience! I recently completed the No-Code AI and Machine Learning: Building Data Science Solutions program offered through MIT Professional Education. The program offered an excellent mix of high-quality video lectures and live mentored sessions, which made advanced AI and Machine Learning concepts very accessible. I gained valuable hands-on experience using tools such as RapidMiner and KNIME, enabling me to build practical data science workflows without writing code. What stood out most was the structured curriculum, real-world case studies, and industry-relevant projects. The program covered important areas such as Generative AI, machine learning techniques, recommendation systems, prompt engineering, and responsible AI practices. The support from mentors and the learning platform made the entire experience smooth, engaging, and highly interactive. Overall, it was a rewarding program that strengthened my understanding of AI and helped me gain practical exposure to building intelligent solutions. Highly recommended for professionals who want to explore AI and data science using a no-code approach.

Read More

Dr. Mary Ann George

Education, Research & Educational services 3-5 yrs

Feb 2026
5.0

Empowering Business Decisions with No-Code AI

I just finished the No Code AI and Machine Learning: Building Data Science Solutions program. With over 30 years of experience as a Chief Information Officer, I recognized the imperative to deepen my knowledge of Artificial Intelligence and related technologies amid rapid disruption. The program's no-code approach was particularly appealing, as I had not coded in a while. I had concerns about online learning platforms, but I quickly became proficient. My colleagues, personal consultant, and weekly face-to-face sessions with an experienced instructor made a significant difference. Overall, it was an excellent experience. I successfully upskilled my knowledge in Artificial Intelligence and Machine Learning, gaining numerous use case examples from my colleagues. Now, I can confidently evaluate business requirements using Artificial Intelligence and Machine Learning paradigms, solving challenges that were previously difficult. This new skill set helps me achieve my professional objectives by gaining greater value from our investments.

Read More

Jesus Marcelino Rocha Gastelum

Farmer's Best

>15 yrs

Jan 2026
5.0

A great learning experience in leading-edge technologies with high-quality instructors and mentors.

TheMIT-No Code AI and Machine Learning program has been a great learning experience in leading-edge technologies. Each module was well designed and rich in technical material, practical examples, and hands-on applications. The program also included project assignments and quizzes to evaluate the learning progress of participants. The quality of the instructors and mentors is very high, and they demonstrated strong subject knowledge along with effective interaction with learners. The structure helped me understand complex AI and ML concepts in an accessible no-code format, making advanced data science feel achievable for professionals from any background. As I approach the end of the program, I can say this experience has helped me develop a better understanding of this field and a clearer view of how to use AI and ML tools in my work. Even though this program was not fully aligned with my specific needs, the journey has been empowering and has provided a strong foundational knowledge base. I would recommend it to any professional looking to apply AI and ML concepts in their work experience.

Read More

Pietro Centoletti

Telecommunications >15 yrs

Oct 2025
5.0

Program made AI truly accessible, expanding my skills and inspiring future tech professionals.

Hi, I am Jorge Rebolledo, an instructor at North West Career College in Las Vegas, and I recently completed MIT No-Code AI and Machine Learning. What motivated me to join was my passion for bringing the latest technology innovations directly into my classroom. My IT students needed to understand how AI and machine learning work in the real world. The impact was immediate. I integrated machine learning concepts into my software development lab, and one of my students built a working solution using Python. I also started using prompt engineering techniques across all four of my classes, and student engagement has been incredible. This experience did not just expand my technical skills; it gave me the tools to inspire the next generation of tech professionals. The program made AI truly accessible to both me and my students, even those just starting their technology journey. I am now better equipped to prepare my students for careers in an AI-driven world, and it has opened up exciting new opportunities for my own career development. I want to thank the instructors for their guidance. I highly recommend this program to any educator or professional ready to embrace the future of technology.

Read More

Jorge Rebolledo

Education, Research & Educational services >15 yrs

Sep 2025

Read All reviews

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Get authentic feedback from our learners sharing their experiences and insights with the course

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"I built a prediction model—something I never imagined when I started training."

I found the AI form insightful for understanding the project through lessons from basic statistics to neural networks. My main takeaway is that no-code tools truly make working on AI easier than expected.

Laurent Laforge

VP Customer success EMEA , Talkwalker
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"The learning modules helped me develop foundation to navigate my research."

I am an AIML researcher and developer who took this class to focus on AIML concepts by removing coding. My experience was fantastic—mentor sessions allowed us to delve deeply into the concepts and collaborate with colleagues.

Samuel Aha Alegria

Owner , The Creative Spirit Incorporated Inc
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"I was able to use the tools and also understand what was going on behind the scenes"

I was fascinated by how deeply we explored model math and algebra—I wasn’t expecting such detailed explanations. I enjoyed the hybrid study style and the real-world case studies. My key takeaway was that no-code tools simplify AI.

Imran Kasam

Global Vice president of Low Code , Avertra Corp

What are the fees for the program?

The course fee is USD 2,850

Invest in your career

14-Week Online Comprehensive Journey: Build multi-agent systems that solve complex business challenges
Structured Learning: Dedicate 8–10 hours weekly to faculty videos, mentor sessions, and hands-on AI projects
Dedicated Mentorship: Build practical AI skills in weekly live online sessions with top Industry Mentors
Earn a globally recognized certificate from MIT PE and get 10 CEUs to validate your AI expertise

Easy payment plans

Avail our EMI options & get financial assistance

Third Party Credit Facilitators

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

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Application Closes: 8th Oct 2026

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Registration Process

Registration close once the required number of participants enroll. Apply early to secure your spot

1. Fill application form

Apply by filling a simple online application form.
2. Application Screening

A panel from Great Learning will review your application to determine your fit for the program.
3. Join program

After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Limited seats available

Learn to design autonomous workflows from MIT Faculty

Batch start date

Online · 10th Oct 2026

Admission closing soon

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Frequently Asked Questions

Program Details

Fee and Payment

Application Process and Eligibility

Why No code and Agentic AI?

Program Details

What are the key learning outcomes of the No Code and Agentic AI program?

By completing the No Code and Agentic AI program by MIT Professional Education, you will develop a future-ready skill set designed to drive real business impact. The curriculum equips you to:

Speak the Language of AI: Understand how AI works, from classic machine learning to autonomous agents, well enough to make informed decisions and hold your own in any AI conversation.
Build Without Writing Code: Set up and use no-code tools to design, run, and test real AI workflows.
Work Intelligently with LLMs: Understand how large language models work and use prompt engineering techniques to get consistently useful, accurate outputs and build Generative AI workflows for automating business processes.
Turn Data into Decisions: Apply clustering, classification, and regression using no-code tools to find patterns, predict outcomes, and support smarter business decisions.
Build AI That Knows What It Doesn't Know: Construct RAG pipelines that connect AI models to real knowledge sources, reducing hallucinations and improving reliability.
Design and Deploy AI Agents: Build autonomous agents that can plan, remember, use tools, and complete multi-step tasks.
Orchestrate Multi-Agent Systems: Design systems where multiple AI agents collaborate and handle real-world complexity, with methods to measure performance.
Evaluate AI Before You Trust It: Use structured methods to assess the quality, accuracy, and reliability of AI outputs before deployment.

What is the program duration?

The program runs for a duration of 14 weeks. The learning journey is structured week-by-week up to Week 14 and includes additional Pre-work (focusing on AI Foundations and No-Code Tool Setup) as well as Self-Paced modules (covering advanced topics like Deep Learning, Computer Vision, and Ethical AI).
The 14-week program consists of 20 hours of recorded video lectures and 14+ live mentored sessions that are 2 hours each.

Is the program completely virtual?

Yes. The program is delivered entirely online, allowing you to learn from anywhere. It is designed to meet the needs of working professionals and enables you to develop practical skills in AI, Machine Learning, and Agentic AI over a 14-week period.

Will I receive a transcript or grade after completion of the program?

No. The No Code and Agentic AI program is a non-degree online certificate course offered by MIT Professional Education in collaboration with Great Learning. As it is not a full-time or credit-bearing university program, official grades or transcripts are not issued.
Participants receive performance marks for each assessment and module to evaluate their understanding and determine eligibility for the certificate. Upon successful completion of the program by achieving a minimum 60 percent score in each individual module, you will be awarded a Certificate of Completion from MIT Professional Education.

What is the application of no-code AI and Agentic AI in different industries?

No-code AI and Agentic AI enable a wider range of business professionals to develop automation solutions and create software applications without prior coding experience. Organizations across sectors such as IT services, education, BFSI, marketing and advertising, FMCG, and manufacturing have adopted no-code AI and machine learning approaches. Here is how leading industries utilize Agentic AI applications and no-code approaches:

Finance: Streamlines processes such as loan approvals and customer experience management. No-code AI helps predict financial risks, anticipate customer churn, and design personalized customer experiences.
Marketing: Supports data analysis and model-building to inform strategic decisions. For example, marketers can segment customer data and lifetime value to tailor targeted campaigns on platforms like Facebook.
Healthcare: Facilitates collaboration between doctors and patients by providing deeper insights into patient health. No-code AI tools enable healthcare professionals to develop customized solutions for patient care.
Education: Helps track courses and manage admissions efficiently. Schools and universities can use no-code AI to handle workloads, expand outreach to students, and improve operational efficiency.
Technology: Enhances cybersecurity by tracing the origin of cyberattacks. Tech professionals can use no-code AI platforms to detect threats and block attackers using data such as port maps.

What are the best No-Code AI tools in the market?

Some of the best AI agent tools and no-code platforms available today include KNIME, n8n, Google AI Studio, NotebookLM, and Claude. This program provides hands-on training with a curated stack of these cutting-edge technologies. You will use platforms like KNIME to build machine learning and regression workflows, and generative tools like Google AI Studio and Claude alongside n8n for agentic design.
To ensure you gain practical experience with these AI agent tools, the program provides exclusive n8n lab access as well as OpenAI API keys for hands-on practice.

Will this program provide similar career outcomes to a program that includes coding like Python?

Yes. The career outcomes of this program are comparable to those of a traditional Data Science and Artificial Intelligence course. You will develop the capability to design data-driven solutions, interpret AI outputs, and apply problem-solving skills to real-world use cases in artificial intelligence and machine learning. While Python and other coding tools are commonly used in traditional programs, this program leverages no-code AI platforms to implement solutions, so programming skills are not required during the learning journey.

What kinds of projects and case studies will I work on in this program?

Throughout the program, you will explore real-world Agentic AI use cases that create tangible business impact. The curriculum includes 14+ case studies across various sectors, such as building a 'Regulatory Intelligence Assistant' for the Healthcare domain to autonomously retrieve and synthesize data.
Additionally, you will complete three official hands-on projects:

Automated Booking Cancellation prediction (Hospitality): Predict which hotel bookings are likely to be cancelled to reduce revenue loss and formulate profitable cancellation policies using clustering, classification, and regression.
Financial Report Analyzer (Finance): Build a RAG pipeline utilizing embeddings and data chunking to help financial analysts extract key information from lengthy annual reports quickly to improve decision-making efficiency.
AI Helpdesk Customer Support: Improve support efficiency by implementing an agentic AI system that classifies tickets, retrieves knowledge, and generates policy-compliant responses with automated escalation.

Does the program reflect the latest technology developments in No-Code AI?

Yes, all the topics in this course are based on the latest technology developments. The program features modules on building workflows on Proprietary Data and Business Context. During the program, you will learn to use cutting-edge No Code tools such as KNIME, n8n, Google AI Studio, NotebookLM, and Claude to build intelligent, autonomous workflows.

How to build AI agents: What tools and frameworks will I learn to use?

To teach you exactly how to build AI agents, the No Code and Agentic AI program by MIT Professional Education utilizes n8n. You will use this tool to design intelligent, autonomous workflows that address complex industry pain points without writing code. This Agentic AI learning path covers highly sought-after conceptual frameworks and skills, including Prompt Engineering, the ReAct framework, RAG, and Agentic AI Evaluation. You will also benefit from exclusive access to n8n Labs for hands-on practice, ensuring you can confidently build and deploy multi-agent systems.

What is n8n, and how will I use it in the program?

n8n is a rapidly growing tool in the market used specifically for building Agentic AI workflows. In this Agentic AI course by MIT Professional Education, you will have an opportunity to use n8n to build end-to-end, no-code agentic workflows, ranging from focused AI assistants to multi-agent systems featuring role-based orchestration and handoffs. To ensure you gain practical experience, the program provides exclusive access to n8n Labs for hands-on practice, provided by Great Learning.

Will I have to spend extra on books, virtual learning materials, or license fees?

No, you will not need to spend extra. All required learning materials are provided online through the program’s Learning Management System. To teach you exactly how to build AI agents and multi-agent systems, the program utilizes tools like n8n and covers frameworks like Prompt Engineering, RAG, and Agentic AI Evaluation. For hands-on practice, you will receive exclusive access to n8n Labs as well as OpenAI API keys provided by Great Learning. A list of recommended resources will also be provided if you wish to explore topics in greater depth.

Fee and Payment

Can my employer sponsor the program fee?

We accept corporate sponsorships and can assist you with the process. For more information, please reach out to us at ncai.mit@mygreatlearning.com.

What is the refund policy?

Please note that submitting the registration fee does constitute enrolling in the program, and the below cancellation penalties will be applied. If you are unable to attend your program, please review our dropout and refund policies below:
Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full.
Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee of USD 250.
Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee.
Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee.
No refund will be made to those who do not engage in the program or leave before completing a program for which they have been registered.
What are my payment options?
You can pay for the program through Bank Transfer and Credit/Debit Cards. You can also opt for easy monthly installments, with flexible, convenient payment terms. Reach out to the registration office at +1 617 860 3529 or +1 844 441 1717 (Toll-Free) to learn more.
For further details, please get in touch with us at ncai.mit@mygreatlearning.com.

Application Process and Eligibility

What are the prerequisites for this No Code and Agentic AI program?

High-school-level understanding of statistics and mathematics is required. No prior coding knowledge is required.

What skills are needed to excel in no-code AI?

No programming, advanced mathematics, or statistical knowledge is required. This program is designed to remove technical barriers, enabling business professionals across marketing, finance, and operations to design and deploy AI solutions themselves.

Is this program right for non-technical professionals?

Yes, it targets professionals in functional roles (Sales, Marketing, Operations) and equips learners where Generative AI can be used to create organizational impact, bridging the strategy-execution gap.

What is the application process?

To apply, complete the online application form. The Great Learning program team will review your submission to assess your fit for the program. If selected, you will receive an offer for the upcoming cohort and can secure your seat by completing the program fee payment.

Why No code and Agentic AI?

Businesses are adopting no-code and agentic approaches to reduce costs, improve efficiency, and accelerate time to market. The no-code approach enables AI and ML for everyone, making processes more scalable. By leveraging Agentic AI, professionals can design intelligent, autonomous workflows—from focused assistants to multi-agent systems with role-based orchestration and handoffs. Even those with no coding experience can now apply these advanced technologies to build smart solutions that drive real business impact.

What is the future of No code and Agentic AI?

The future of Agentic AI is defined by unprecedented growth and accessibility. The global AI agents market was valued at $7.63 billion in 2025 and is projected to surge to $182.97 billion by 2033, growing at a CAGR of 49.6%. Alongside this market boom, AI fluency has become the fastest-growing skill category in U.S. job postings, with demand growing sevenfold in just two years.
For non-technical professionals, the no-code angle is incredibly compelling. This means the ability to design and deploy intelligent, autonomous workflows is no longer reserved for engineers, enabling business leaders to drive real impact without writing a single line of code.

Delivered in Collaboration with:

MIT Professional Education is collaborating with online education provider Great Learning to offer No Code and Agentic AI. This program leverages MIT's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning collaborates with institutions to manage enrollments (including all payment services and invoicing), technology, and participant support. Accessibility

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +1 844 441 1717 or email to ncai.mit@mygreatlearning.com

Build AI Agents and Intelligent Workflows Without Coding

No Code and Agentic AI

Delivered in collaboration with Great Learning

No Code and Agentic AI program

Why should you join this program?

No Code Program to Build AI Agents & Intelligent Workflows

Built on MIT’s Legacy of Innovation

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Learn from MIT faculty

Attend Mentorship Sessions by Industry Experts

Innovate Using No-Code Tools