Build AI Solutions Without Coding Expertise
No Code AI and Machine Learning: Building Data Science Solutions
Application closes on
16th Oct 2025
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4.71
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+1 844 441 1717
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No Code AI and Machine Learning: Building Data Science Solutions
Build industry-valued AI and Machine Learning skills with no code approach
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4.71 ( 5331 Ratings )
Application closes 16th Oct 2025
Why No Code AI?
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Booming Industry Demand
The need for AI adoption in businesses is higher than ever and no code platforms are bridging this gap by allowing professionals to build AI solutions without technical expertise.
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The No Code Advantage
Mastering no code AI tools allows technical and non-technical professionals to lead innovation initiatives without relying on data science teams and to validate AI ideas before committing large budgets.
Program Outcomes
Key takeaways for career success in AI and Machine Learning
Designed for learners to leverage Machine Learning (ML), Generative AI (GenAI), and advanced Agentic AI, without writing a single line of code
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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.
Earn a certificate of completion from MIT Professional Education
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World #1
QS World University Rankings 2025
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U.S. #2
U.S. News & World Report 2025
KEY PROGRAM HIGHLIGHTS
Why choose the No Code AI and Machine Learning 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
Get access to Generative AI modules on Prompt Engineering, Retrieval Augmented Generation (RAG), and Agentic AI.
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Build your AI and Machine Learning 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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Empower Your Innovation with No Code Tools
Gain the skills to design and deploy AI-driven solutions using no code platforms like RapidMiner, and Teachable Machine.
Skills you will learn
Artificial Intelligence
Machine Learning
Deep Learning
Prompt Engineering
Data Analysis
Generative AI
Agentic AI
Retrieval-Augmented Generation (RAG)
Data Visualization
Computer Vision
Supervised and Unsupervised Learning
Model Evaluation & Tuning
Recommendation Systems
Responsible AI
Artificial Intelligence
Machine Learning
Deep Learning
Prompt Engineering
Data Analysis
Generative AI
Agentic AI
Retrieval-Augmented Generation (RAG)
Data Visualization
Computer Vision
Supervised and Unsupervised Learning
Model Evaluation & Tuning
Recommendation Systems
Responsible AI
view more
- Overview
- Curriculum
- Projects
- Tools
- Certificate
- Faculty
- Reviews
- Fees
- FAQ
This program is ideal for
Professionals from technical and non-technical backgrounds ready to advance their skills in AI
View Batch Profile
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Non-Tech Professionals Entering AI
Learn to drive AI initiatives, lead AI and data-driven teams, and spearhead innovation using AI technologies using no code tools.
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Leaders Leveraging AI for Innovation
Understand how AI and Machine Learning solutions can be developed and integrated to accelerate business growth and drive innovation.
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Managers Driving Rapid AI Deployment
Discover how to quickly build and launch AI-powered solutions and prototypes without heavy reliance on large data science teams.
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Consultants Expanding into AI Strategy
Build working prototypes or early solutions without needing large data teams, using no code platforms, and hands-on prototyping.
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: Introduction to Data Science and AI
This is a foundational step in your program journey. It'll get you acquainted with the Data Science and AI world and equip you with basic No Code tool skills, thereby laying a foundation for learning throughout the program.
- Discovering the fascinating history of Data Science and AI
- Transforming industries through Data Science and AI
- The math and stats underlying the technology
- Navigating the Data Science lifecycle
- Introduction to AI Studio (RapidMiner) and KNIME
Week 1: Introduction to the AI and Generative AI Landscape
In this week, you will embark on an insightful journey through the evolving world of artificial intelligence. You’ll explore AI’s history, its role in organizations, data operations, and strategic approaches to building AI products for driving innovation and efficiency.
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AI and generative AI landscape: history and landscape
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Organizations, people, and data
- Data operations in various organizations
- Strategy for building AI products
Week 2: Data Exploration - Structured Data
In this week, you will gain practical insights into analyzing and interpreting structured data using advanced techniques. By mastering these methods, you'll be well-prepared to apply data exploration techniques to drive data-driven decisions and insights within your organization, significantly enhancing your strategic and analytical capabilities.
- Clustering (K-means clustering, K-medoids, Gaussian mixture)
- Dimensionality reduction techniques (PCA, t-SNE)
Week 3: Prediction Methods – Regression
In this week, you’ll explore regression techniques like Linear Regression, K-Fold Cross-Validation, Bootstrapping, and LOOCV to build accurate predictive models. You’ll also learn to validate assumptions and enhance model reliability for data-driven forecasting.
- Linear Regression
- Assumptions of Linear Regression
- K-Fold CV
- Bootstrapping
- LOOCV
Week 4: Decision Systems
In this week, you’ll dive into classification techniques like Decision Trees and Random Forests to make accurate predictions from categorical data. You'll also learn to evaluate model performance using the Confusion Matrix, Information Gain, and the Bias-Variance trade-off.
- Decision tree
- Bagging
- Random forests
Week 5: Project Week - Machine Learning Classification
Week 6: Recommendation Systems
In this week, you will learn to develop powerful tools that personalize user experiences, a key asset in today's data-driven landscape. You’ll explore Recommendation Systems through clustering, collaborative filtering, rank-based, and content-based methods to design impactful, user-centric solutions.
- Recommendation systems: problem statements and solutions
- Clustering-based recommendation systems
- Collaborative filtering
- Rank-based and content-based techniques
Week 7: Prediction Methods – Neural Networks
In this week, you’ll embark on an exploration of deep learning, mastering core concepts, neural network building blocks, and training techniques. You'll apply your knowledge to a practical case study in Digit Recognition, mastering a classic deep learning application.
- Introduction to deep learning
- Building blocks of neural networks
- Training neural networks
- Digit recognition case study
Week 8: Computer Vision Methods
In this week, you'll dive into the fascinating world of machines that can see and interpret visual data. You’ll explore CNN building blocks, training techniques, and practical applications like image detection and object recognition.
- Drawbacks of artificial neural networks (ANNs)
- Building blocks of convolutional neural networks (CNNs)
- Training convolutional neural networks
- Image detection
Week 9: Project Week - Neural Networks
Week 10: Generative AI Foundations
In this week, you’ll learn the foundational concepts of Generative AI, beginning with an exploration of its origins and the underlying principles behind generating new data. You’ll also explore matrix estimation perspectives, large language models as probabilistic sequence predictors, and key prompt engineering techniques.
- Origins of generating new data
- Generative AI as a matrix estimation problem
- Large language models as probabilistic models for sequence completion
- Prompt engineering
Week 11: Business Applications of Generative AI
In this week, you’ll dive into the business applications of Generative AI, including Retrieval-Augmented Generation (RAG) for improving response relevance and Agentic AI for autonomous decision-making.
Natural language tasks with generative AI
Summarization, classification, and generation
Retrieval-augmented generation (RAG)
Agentic AI
Week 12: Ethical and Responsible AI
In this week, you’ll explore the ethical and responsible development of AI systems, from the AI lifecycle to addressing bias, causality, and privacy concerns. You’ll also examine how AI components interconnect and influence each other across various domains and complex systems.
- Introduction to the AI lifecycle
- Introduction to bias and its examples
- Introduction to causality and privacy
- Interconnections and domains
- Interdependency and feedback in AI systems
Self-Paced Modules
In these self-paced modules, you'll learn to navigate and extract insights from unstructured data, a crucial competency in today's data-rich environment. You will also explore the specialized techniques needed to analyze time-dependent data, a vital skill for predicting trends and making strategic decisions.
- Data Exploration: Unstructured Data (text processing, sentiment analysis, text classification)
- Data Exploration: Temporal Data (time series methods, ARIMA modeling)
Projects and Case Studies
The program follows a learn-by-doing pedagogy, helping you build your skills through real-world case studies and hands-on practice. Below are samples of potential project topics and case studies you will work on.
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3
hands-on projects
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20+
case studies
Hospitality
Hotel Booking Cancellation
Description
This project focuses on reducing the financial impact of last-minute hotel booking cancellations. INN Hotels Group, operating in Portugal, faces challenges due to a high volume of cancellations, leading to revenue loss, increased distribution costs, and reduced profit margins. By analyzing booking patterns and customer behavior, a predictive model is developed to identify likely cancellations in advance. This enables 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
Marketing and Advertising
GenAI-powered Review Categorization
Description
This project 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
- Microsoft Copilot
- ChatGPT
- Perplexity
EdTech
Sales Leads Conversion Prediction
Description
This case study focuses on building a machine learning solution to help an EdTech startup, ExtraaLearn, identify high-potential leads from a large pool of incoming prospects. By analyzing user interaction data from digital platforms, the solution predicts which leads are most likely to convert into paying customers, allowing for better resource allocation and targeted engagement. The case also highlights key insights into the behavioral patterns that influence lead conversion in online education.
Skills you will learn
- Exploratory Data Analysis (EDA)
- Data Preprocessing
- Decision Tree
- Data Visualization
Healthcare
Hospital LOS Prediction
Description
This case study 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
No code tools covered
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KNIME
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RapidMiner
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Teachable Machine
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ChatGPT
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Gemini
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NotebookLM
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Dall E
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.
Program Faculty
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Stefanie Jegelka
Associate Professor, EECS and IDSS
Expert in algorithms and optimization for AI.
Pioneer advancing theoretical machine learning foundations.
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 -
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 -
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 -
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
LinkedIn Profile
University LinkLearners review our program support & mentors
Get authentic feedback from our learners sharing their experiences and insights with the course
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The program significantly improved my understanding of Machine Learning and Data Science.
I recently completed a program focused on Artificial Intelligence, Machine Learning, and Deep Learning. My goal was to gain a deeper understanding of these fields, including their history, foundations, and role in various industries. I was pleasantly surprised by the hands-on approach to modeling, problem-solving, and data analysis. As someone who works closely with Data Scientists and Software Engineers but isn't a Data Scientist myself, I found the pre-work module extremely valuable for getting up to speed with relevant statistics and mathematical concepts. The content delivery platform was also helpful for tracking progress and planning ahead. The collaborative environment allowed me to connect with other students, enhancing the learning experience. Overall, this program significantly improved my understanding of Machine Learning and Data Science, complementing my leadership skills and improving cross-functional collaboration in my job.
Read More
Rosie Manfredi
srmanager product management, user experience & optimization testing ebusiness , hd supply
2nd Jul 2023
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Enrolling in this program was the best decision I ever made.
Enrolling in this program turned out to be the best decision I ever made. The staff at Great Learning and MIT ensured that I had all the tools needed to successfully complete this program. The pre-work module allowed me to refresh the basic concepts of statistics and to get familiar with RapidMiner. The mix of mentored sessions and recorded video lectures was really effective. I greatly enjoyed the projects where I got to build algorithms from scratch and evaluate their performance. My Program Manager was always available and quick to respond to my queries.
Read More
Filippo Turrini
export manager , stm srl
26th Jun 2023
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A challenging but rewarding journey with excellent support from the Program Manager.
The No Code AI and Machine Learning program was a challenging but rewarding journey. The program was well-structured, with excellent support from the Program Manager and valuable mentor sessions. These resources helped clarify any doubts or questions I had. My dedication and commitment to understanding the material were crucial to my success in the program. I hope my experience inspires others to pursue their goals in Machine Learning or any other field.
Read More
Xinyi Huang
partner , flamestone investment corp.
26th Jun 2023
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Great Learning offered a seamless journey with expert faculty and mentor support.
Holding a Bachelor's in Electrical Engineering, I've held management roles. Eager to contribute to my organization, I chose the program to develop Data Science solutions for gaming. Great Learning provided a seamless learning journey with great expertise of faculty and support from mentors. The program's key takeaways include building prototypes and making data-driven decisions.
Read More
Joseph Julian M. Cacha
vice president for operations , 888 world solutions inc.
26th Jun 2023
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The projects helped me build strong hands-on knowledge in Data Science.
My learning journey has been enriching. I have received great insights from the faculty throughout the program. Particularly, the projects that I worked on have helped me build strong hands-on knowledge in Data Science.
Read More
Ayoub Mounaddam
ceo&senior executive partner , digital impulsion
18th Mar 2023
Watch inspiring success stories
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Watch story
"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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Watch story
"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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Watch story
"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
Course fees
The course fee is 2,850 USD
Invest in your career
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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.
Easy payment plans
Avail our EMI options & get financial assistance
Third Party Credit Facilitators
Check out different payment options with third party credit facility providers
*Subject to third party credit facility provider approval based on applicable regions & eligibility
Application Process
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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.
Batch start date
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Online · To be announced
Admissions Open
Frequently asked questions
What is the required weekly time commitment?
The program is divided into 10 modules, with a total of 80 study hours. Most participants are expected to spend an average of 6-12 hours per week on program activities.
Is the program completely virtual?
Yes, the program has been designed to meet the needs of working professionals so that you can learn how to leverage AI and machine learning methods from the convenience of your home within 12 weeks.
Will I receive a transcript or grade after completion of the program?
No, the No Code AI and Machine Learning Program is an online program offered by MIT Professional Education - Digital Plus Programs in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, there are no grades or transcripts for this program. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate of completion. Upon successful completion of the program i.e. after achieving 80% in each course module as per the eligibility of the certificate, you are issued a certificate of completion from MIT Professional Education.
What is the application of no-code AI in different industries?
No code AI has allowed a broader range of business employees to own their automation and build new software applications without coding experience. Various sectors such as ITeS, Education, BFSI, marketing, advertising, FMCG, and manufacturing have adopted the no-code AI and ML approaches. Here’s how leading industries are leveraging no-code AI approaches:
- Finance: Helps streamline many processes like loan decisions and customer experience for banks and financial institutions. Predicts financial risks, customer churn prediction, and plans a better customer experience.
- Marketing: Assist in building models to craftily sort and analyze data in meaningful ways to make informed decisions. For example, marketers can segregate data about customer activities and lifetime value using no-code AI to tailor a Facebook ad to find a potential customer.
- Healthcare: Encourages new collaboration between doctors and patients to give unprecedented insight into patient health, the no-code AI tools empower healthcare professionals to build customized healthcare solutions.
- Education: Keep track of the courses offered to the registrants to streamline the entire admission process. The No-Code approach can help schools keep up with the workload, improve their reach to students and increase overall efficiency across the university
- Technology: Trace where a cyber attack is coming from. Tech professionals can utilize No Code AI platforms to detect attackers and block them by using OGS of port map data.
What are the best No Code AI tools in the market?
RapidMiner, KNIME, Ikigai, and Teachable Machine are some of the best open-source, free-to-use, No-Code AI tools in the market. Cloud platforms like Amazon Web Services also offer free tiers to carry out a limited amount of exploration using the No Code AI tools.
Will this program provide similar career outcomes to a program that includes coding like Python?
The outcomes of this program would be similar to any Data Science program, i.e., to build the capability to develop data-driven solutions, interpret data outputs like an AI consumer, and develop problem-solving skills for use cases in Artificial Intelligence and Machine Learning. Python and RapidMiner are merely the tools utilized to implement these solutions. The only difference is that this program would not require you to develop programming skills during the learning journey, as the implementations are carried out using No Code AI tools.
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 AI and Machine Learning Program is an online program offered by MIT Professional Education - Digital Plus Programs in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, there are no grades or transcripts for this program. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate. Upon successful completion of the program, i.e. after completing all the modules as per the eligibility of the certificate, you are issued a certificate from MIT Professional Education.
Will I have to spend extra on books, virtual learning materials, or license fees?
No. All required learning material is provided online through our Learning Management System. But given this field is vast and ever-changing, there is always more you can read and there will be a list of recommended books and other resources made available to you for your additional reading pleasure.
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.
What are the prerequisites for this No Code AI and Machine Learning program?
The prerequisites of the program include fundamentals of mathematics and statistics. If you do not possess either (or both) of them, you are expected to put in extra effort to learn them before the commencement of the program to be able to cope with the curriculum by MIT Professional Education. We, from Great Learning, will provide you with pre-work including sessions by Dr. Abhinanda Sarkar (Learning Instructor - Prework) and Dr. Georg Huettenegger (Industry Professional - Prework) that can be useful in understanding the fundamentals of mathematics and statistics.
What skills are needed to excel in no-code AI?
No programming or advanced mathematics knowledge is required to participate in the No Code AI and ML program. Familiarity with basic statistics is recommended to get the most out of the program.
What is the Application process?
Simply complete your online application form and then the Great Learning program team will review it to determine your fit with the program. If selected, you will receive an offer for the upcoming cohort and can then secure your seat by paying the fee
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
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4.71 ( 5331 Ratings )
Application Closes
16th Oct 2025
Delivered in Collaboration with:
MIT Professional Education is collaborating with online education provider Great Learning to offer No Code AI and Machine Learning: Building Data Science Solutions. 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
Batch Profile
The Data Science and Machine Learning class consists of working professionals from excellent organizations and backgrounds maintaining an impressive diversity across work experience, roles and industries.
Industry Diversity
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