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No Code and Agentic AI program

No Code and Agentic AI program

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Application closes 7th May 2026

Why No Code and Agentic AI?

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    Booming Industry Demand

    AI has become a critical component of business, and no-code platforms are closing the gap by enabling professionals to build AI solutions without technical expertise

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    The No Code Advantage

    Use no-code AI tools to apply and understand core concepts, enabling both technical and non-technical professionals to lead innovation initiatives without relying on data science teams.

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PROGRAM OUTCOMES

Elevate Your Career With No-Code AI

Build proficiency in ML, GenAI, and Agentic AI without writing a single line of code.

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    Build autonomous agents capable of planning, memory, tool use, and executing multi-step tasks.

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    Design systems where multiple AI agents collaborate on complex tasks and measure performance.

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    Transform data into actionable insights using intuitive, no code platforms.

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    Rapidly prototype, test, and operationalize machine learning models without writing code.

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    Leverage supervised and unsupervised learning, recommendation systems, deep learning, and computer vision.

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    Utilize Generative AI, Prompt Engineering, and Agentic AI to design intelligent, autonomous workflows.

KEY PROGRAM HIGHLIGHTS

Why Choose This Program

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    Learn from MIT faculty

    Learn from the vast knowledge of 5 award-winning MIT faculty and instructors through recorded sessions.

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    Industry-Relevant Curriculum

    Build AI agents, prototypes, and intelligent workflows that drive innovation and enhance productivity.

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    Build your AI Portfolio

    Build a portfolio featuring 3 industry-relevant projects to showcase your practical AI capabilities.

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    Personalized Mentorship Sessions

    Get mentorship from industry experts in Data Science and Artificial Intelligence based on the concepts taught by MIT faculty.

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    Dedicated Program Support

    Connect with dedicated program managers to assist with queries and guide you throughout the course.

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    Innovate With No Code Tools

    Gain the skills to design and deploy AI-driven solutions using no code platforms like KNIME, and N8N

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

LLM Intergation

Ethical and Responsible ai

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

LLM Intergation

Ethical and Responsible ai

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  • Overview
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Fees
  • FAQ
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This Program is Ideal For

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

View Batch Profile

  • 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.

Experience a Unique Learning Journey

Our pedagogy is designed to ensure career growth and transformation

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    Learn from world-renowned faculty

    Learn critical concepts through recorded video lectures from award-winning MIT professors

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    Engage with your mentors

    Participate in live sessions led by industry experts and build application-ready skills

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    Work on hands-on projects

    Build a portfolio of industry-relevant projects and case studies to showcase skills

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    Get personalized assistance

    Our dedicated program managers will support you through your learning journey

Curriculum

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 problem

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

Sample Projects and Case Studies

Hands-on practice ensure that learners gain tangible outcomes and highly effective skills.

  • 3

    hands-on projects

  • 14+

    case studies

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Hospitality

Hotel Booking Cancellation

Description

Focuses on minimizing the financial impact of last-minute hotel booking cancellations by analyzing booking patterns and customer behavior. A predictive model is developed to identify likely cancellations in advance, enabling the hotel chain to implement effective cancellation policies and optimize resource planning.

Skills you will learn

  • KNIME
  • RapidMiner
  • Decision Trees
  • Random Forest
  • Exploratory Data Analysis (EDA)
  • Data Preprocessing
  • Data Visualization
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Marketing and Advertising

GenAI-powered Review Categorization

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

  • Prompt Engineering
  • ChatGPT
  • Perplexity
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HEALTHCARE

Agentic Regulatory Intelligence Assistant

Description

Build a no-code, agent-driven system using n8n to automatically monitor global regulatory bodies (FDA, EMA, WHO), extract and summarize key medical device updates, and validate information quality to reduce compliance risk and eliminate manual monitoring.

Skills you will learn

  • Prompt Engineering
  • Building No Code Agentic AI workflows
  • ReAct Framework
  • Agentic AI Evaluation
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EdTech

Sales Leads Conversion Prediction

Description

Focuses on building a machine learning solution to help an EdTech startup identify high-potential leads from a large pool of incoming prospects. By analyzing user interaction data from digital platforms, the solution highlights key insights into the behavioral patterns that influence lead conversion in online education.

Skills you will learn

  • Exploratory Data Analysis (EDA)
  • Data Pre-processing
  • Decision Tree
  • Data Visualization
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healthcare

Hospital LOS Prediction

Description

Focuses on building a regression-based machine learning solution to predict the Length of Stay (LOS) of patients using data available at admission and from initial tests. The goal is to identify key factors influencing LOS, derive actionable insights, and support hospital policy planning to enhance infrastructure and revenue generation.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Regression Modeling
  • Data Interpretation
  • KNIME
  • RapidMiner
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Food & Nutrition Tech

FitFuel Protein Bars - Marketing Material Generation

Description

Explores how a leading player in the health and nutrition space, harnessed Generative AI to transform its digital marketing approach. By generating a dynamic, audience-tailored product page for its new line of protein bars, FitFuel addressed the challenge of market differentiation and customer engagement.

Skills you will learn

  • Generative AI
  • Prompt Engineering
  • Poe
  • ChatGPT
  • Perplexity
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E-commerce

Yelp Recommendation System

Description

Explores designing and evaluating a recommendation system using Yelp review data to address the problem of information overload in e-commerce. By leveraging user-generated feedback such as ratings and textual reviews, the system predicts user preferences and recommends businesses—ranging from restaurants and salons to healthcare services.

Skills you will learn

  • Data Visualization
  • Data Pre-processin
  • Recommender Systems
  • Knowledge-Based and Rank-Based Filtering
  • Similarity-Based Collaborative Filtering
  • Model Evaluation

No-Code Tools Covered

Gain hands-on experience with no code tools to optimize models and build innovative solutions

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    KNIME

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    NotebookLM

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    n8n

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    Google AI Studio

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    Claude

Earn a Certificate of Completion from MIT Professional Education

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

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* Image for illustration only. Certificate subject to change.

Program Faculty

Meet our expert faculty & professionals with in-depth AI & ML knowledge and a passion to help you succeed

  • Munther Dahleh - Faculty Director

    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 - Faculty Director

    Stefanie Jegelka

    Associate Professor, EECS and IDSS

    Expert in algorithms and optimization for AI.

    Pioneer advancing theoretical machine learning foundations.

    Know More
  • Devavrat Shah - Faculty Director

    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 - Faculty Director

    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 - Faculty Director

    Caroline Uhler

    Professor, EECS and IDSS

    Expert in computational biology, statistics, and systems.

    Award-winning scholar relentlessly driving transformative data insights.

    Know More

Program Mentors

Get guidance from experienced AI and data science experts. Mentor list may change based on availability.

  • Cristiano Santos De Aguiar - Mentor

    Cristiano Santos De Aguiar

    Data Scientist, Bresotec Medical
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  • Peyman Hessari - Mentor

    Peyman Hessari

    Senior Data Scientist, ATB Financial
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  • Jatin Dawar - Mentor

    Jatin Dawar

    Senior Machine Learning Engineer, Telus
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  • Olabode James - Mentor

    Olabode James

    Machine Learning Architect, Rubik Technologies
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Course Fees

Invest in your career USD 2,850

Course fees

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    Transform data into actionable insights using intuitive, no code platforms.

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    Rapidly prototype, test, and operationalize machine learning models without writing code.

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    Utilize Generative AI, Prompt Engineering, and Agentic AI to design intelligent, autonomous workflows.

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    Earn a certificate of completion from MIT Professional Education, and 10 Continuing Education Units (CEUs)

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Easy payment plans

Avail our EMI options & get financial assistance

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

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Application Closes: 7th May 2026

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Application Closes: 7th May 2026

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Registration Process

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

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    1. Fill application form

    Apply by filling a simple online application form.

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    2. Application Screening

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

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    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

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Batch start date

  • Online · 16th May 2026

    Admission closing soon

    Apply Now

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

No Code and Agentic AI program

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phone icon Application Closes 7th May 2026

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

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Frequently asked questions

Program Details
Fee and Payment
Application Process and Eligibility
No code AI and machine learning
Program Details

What is the required weekly time commitment?

The program consists of 10 modules, totaling approximately 80 study hours. Most participants can expect to spend an average of 6 to 12 hours per week on program activities.

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 and machine learning over a 12-week period.

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

Some of the top open-source and free-to-use no-code AI tools include RapidMiner, KNIME, and Teachable Machine. Cloud platforms such as Amazon Web Services also offer free tiers for limited exploration and experimentation with no-code AI solutions.

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

No-code AI enables 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’s how leading industries are leveraging no-code AI 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.

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 program. 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?

The case studies and projects are based on multiple industry sectors, including Education, Healthcare, IT, Finance, Retail, Research, and many more.

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 in No Code AI. The program includes multiple No Code tools such as RapidMiner, Dataiku, KNIME and Teachable Machine, which you can use to implement business solutions to various data modalities and problem statement paradigms in Artificial Intelligence and Machine Learning.

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

No. The No Code and Agentic AI program : Building Data Science Solutions Program is a non-degree online program 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 score of 80 percent in each module, you will be awarded a Certificate of Completion from MIT Professional Education.

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

No. All required learning materials are provided online through the program’s Learning Management System. However, because the field of AI is vast and constantly evolving, a list of recommended books and additional resources will be provided for those who 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 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?

The program requires a foundational understanding of mathematics and statistics. If you are not familiar with these areas, it is recommended that you review them before the program begins to keep pace with the curriculum from MIT Professional Education. Great Learning provides pre-work resources, including sessions by Dr. Abhinanda Sarkar (Learning Instructor) and Dr. Georg Huettenegger (Industry Professional), to help you strengthen your understanding of these fundamentals.

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

No programming or advanced mathematics knowledge is required to participate in the No Code and Agentic AI program : Building Data Science Solutions Program. Familiarity with basic statistics and mathematics is recommended to maximize your learning experience and effectively apply the concepts taught in the program.

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.
No code AI and machine learning

Why no code AI and machine learning?

Businesses are starting to adopt no-code approaches to reduce costs, improve the efficiency of their existing solutions, and accelerate time to market. The no-code approach enables AI and ML for everyone, making processes more scalable. Even professionals with no coding experience can now apply these advanced technologies to build intelligent solutions and help make informed decisions.

What is the future of no-code AI and machine learning?

The post-pandemic shift has led to increased adoption of digital technologies. Gartner projects a 23% increase in the global market for no-code tools and development. There is a steady growth in the use of no-code approaches due to their effectiveness in addressing some of tech’s most significant challenges—digitizing workflows, improving customer and employee experiences, and boosting the efficiency of operational teams