Curriculum
- Curriculum designed by MIT faculty in Data Science and Machine Learning
- Become a Data Science decision maker by learning Deep Learning, Machine Learning, Recommendation Systems, and more.
The No Code AI and Machine Learning: Building Data Science Solutions Program lasts 12 weeks. The program will begin with blended learning elements, including recorded lectures by MIT Faculty, case studies, projects, quizzes, mentor learning sessions, and webinars.
Certificate of Completion from MIT Professional Education
Hands-on Projects
Following a learn by doing pedagogy, the No Code AI and Machine Learning Program offers you the opportunity to apply your skills and knowledge in real-time through 3+ industry-relevant projects and 15+ real-world case studies.
Below are samples of potential project topics.
Real Estate
Melbourne Housing Data Analysis
Area of Project
Exploratory Data Analysis
Small Summary
To extract insights on the key factors that affect the selling price and the other characteristics of houses in Melbourne Australia
Tools & Concepts: RapidMiner, EDA, Descriptive Statistics
Learn more
Entertainment
Showtime Viewership Prediction
Area of Project
Prediction methods - Regression
Small Summary
To analyze the data and start off by coming up with a Linear Regression model to determine the driving factors for first-day viewership.
Tools & Concepts: Rapidminer, Linear Regression
Learn more
Hotels
HotelBooking Cancellation Prediction
Area of Project
Decision System
Small Summary
To predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
Tools & Concepts: Rapidminer/Dataiku/ KNIME, Decision tree, Random Forest
Learn more
Sales
Sales Lead Conversion prediction
Area of Project
Decision System
Small Summary
To build an ML model to help identify which leads are more likely to convert to paid customers
Tools & Concepts: Rapid Miner, Data pre-processing, EDA, Linear regression
Learn more
Healthcare
Hospital Length-Of-Stay prediction
Area of Project
Prediction Methods - Regression
Small Summary
To predict the LOS of a patient using the data available during admission and after running a few tests.
Tools & Concepts: Rapidminer/Dataiku/KNIME, Linear Regression
Learn more
E-Commerce
Product Recommendation System
Online E-commerce websites like Amazon use different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time. In this project, you will use similar concepts to create your own product recommendation system.
Tools & Concepts: Content-Based Recommendation Systems, Collaborative Filtering, Python
Learn more
Health care
Brain Tumor Identification
Area of Project
Computer Vision Methods
Small Summary
To Classify MRI Scans into two classes i.e., healthy or having brain tumor.
Tools & Concepts: Teachable Machine, ANN, CNN
Learn more
Telecom
Spam detection
Area of Project
Data Exploration Unstructured Data
Small Summary
To extract meaningful insights from the data and build a classification model to predict whether an SMS is 'spam' or not
Tools & Concepts: Rapidminer/Dataiku, Text preprocessing, Sentiment analysis, Bag of word, Lemmatization, Vectorization
Learn more
MIT Faculty
Gain insights from world-renowned MIT faculty through recorded sessions on Data Science, Machine Learning, and Artificial Intelligence.
Program Faculty
![Professor Munther Dahleh]( "Professor Munther Dahleh")
Munther Dahleh
Program Faculty Director, MIT Institute for Data, Systems, and Society (IDSS)
![Professor Stefanie Jegelka]( "Professor Stefanie Jegelka")
Stefanie Jegelka
X-Consortium Career Development Associate Professor, EECS and IDSS, MIT
![Professor Devavrat Shah]( "Professor Devavrat Shah")
Devavrat Shah
Professor, EECS and IDSS, MIT
![Professor John N. Tsitsiklis]( "Professor John N. Tsitsiklis")
John N. Tsitsiklis
Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT
![Professor Caroline Uhler]( "Professor Caroline Uhler")
Caroline Uhler
Henry L. & Grace Doherty Associate Professor, EECS and IDSS, MIT
Learner Testimonials
Mentored learning sessions, video content and explanation are good. Excellent, deep enough but not too deep for beginners. The pre-work course was and other modules document content were also very helpful as well as being very helpful to the students in the classroom.
Christian Ntsiba Gassuet
Data Engineer at National Grid
The MIT No Code machine learning and artificial intelligence course with Great Learning is a well-paced, highly engaging and useful course. I highly recommend this course to anyone looking for a thought-provoking course that will give you the tools you need to bring a competitive edge into your workplace.
Zai Ortiz
Technical Writer at Wizeline
This is the most informative, practical and foundational machine learning course I've taken so far. All the lecturers did a very good job at condensing the material into small videos that has multiple small quizzes and conclusion. Overall, it's a wonderful learning experience.
Vincent Li
Product Engineer at Qualcomm
Got immense support from my program manager, fantastic mentors, and professors. When I started the pre-work, it was a daunting challenge to manage my work alongside this course. By the time I went through the first module, I had an understanding of how to multi-task effectively.
Sylvia Jayakaran
Senior Manager - Artificial Intelligence Operations (AIOps) at Servus Credit Union
The assessment really tested our knowledge on the subject and foundations along with doing a project, that helped us with hands-on implementation.The key learnings for me to understand how recommendation engines are built on e-commerce websites and how classification models can help in managing fraud for a payment firm.
Sasikanth Nagalla
Payments Risk Data Science at Stripe
I joined the program with an interest in the AI and ML field but no previous knowledge. The program gave me a complete overview and understanding of the field and methodologies. The reliance on projects either as case studies during weekly sessions or as individual assignments is a real plus
Rubbens Parchet
Program Manager Sustainability at Philip Morris International
The learner management system made it all easy to navigate. The mentoring sessions were helpful and brought another perspective to the topic. I liked the curated articles, and the projects were reasonable as well.
Linda Drabova
business advisor at Metaorigin Labs
Your Learning Experience
The No Code AI and Machine Learning: Building Data Science Solutions serves as a platform for business leaders and AI enthusiasts to leverage an application-based pedagogy and solve real-world business problems.
Self-paced recorded sessions curated by MIT Faculty
- Flexible Self Paced learning with recorded lectures
- Designed and Delivered by MIT faculty
- Learn to make AI-backed decisions for business growth
Mentorship and Program Support
- Live mentorship by industry experts
- Small groups of learners for personalized guidance and support
- Interaction with like-minded peers from diverse backgrounds and geographies
- Get personalized assistance with a dedicated Program Manager
Build your Data Science and AI Portfolio
- 3+ industry-relevant projects and 15+ real-world case studies to gain practical experience
- Translate learnings into solution-oriented skills
Learner Feedback on Mentorship and PM Support
No Code AI and Machine Learning: Building Data Science Solutions
Learner Satisfaction Rating
5,331Ratings
All Reviews
Luz Elena Narvaez Hernández
Batch of
| Secretary of Aerospace Planning and Innovation
at Universidad Aeronáutica en Querétaro
| Mexico
The program is excellent, with high-quality, easy-to-follow material. The extensive resources provided and excellent follow-up from tutors and teachers make learning accessible and comprehensive. I highly recommend this program.
READ MORE
Antonio Nascimento Francisco
Batch of
| CEO
at Antonio Nascimento Francisco Soc Unip Lda
| Switzerland
Taking MIT's No Code AI and Machine Learning program was a transformative experience. From the first module, I was captivated by the clear and accessible presentation of AI concepts. The hands-on projects were incredibly impactful, allowing me to apply my learning in real time.
As the weeks went by, I started to see how to use these new skills to enhance the customized CRM solutions I developed for my clients. The program opened my eyes to the possibilities of Generative AI, and I utilized CRM and online store data to create more intelligent, responsive solutions.
I was able to develop a Generative AI-based tool that significantly improved client engagement and sales conversions. This directly resulted from the insights and skills I gained from the program.
Today, I confidently use AI to drive innovation in my work, and I owe it all to the comprehensive training I received from the program. I wholeheartedly recommend this program to anyone who wants to understand AI and apply it in their everyday lives. Whether you're an entrepreneur looking to innovate or a professional aiming to enhance your company's capabilities, this program is invaluable.
READ MORE
Anna Santos
Batch of
| Fod scientist, nutritionist
at University College Cork
| Ireland
The program's curriculum was thorough, covering various Artificial Intelligence and Machine Learning topics. What stood out to me was the "no code" approach, which made the material accessible even to someone like me with limited coding experience. The faculty excelled at breaking down complex concepts for easy understanding.
The program's structure and organization were impressive. Each module was thoughtfully arranged, building upon the previous one in a logical sequence that enhanced my understanding and gave me a real sense of accomplishment. The program's flexibility allowed me to balance my learning with other commitments, which was a significant advantage.
One of the experience's highlights was the team's unwavering support. My Program Manager, in particular, was a constant source of guidance and encouragement. Whether I had questions or needed project assistance, help was always available.
I recommend Great Learning's No Code AI and Machine Learning program. Its structured and organized approach, exceptional content, and supportive team equipped me with valuable skills and ignited a passion for Artificial Intelligence and Machine Learning. Thank you, Great Learning, for this unforgettable journey!
READ MORE
WALDON ANTONIO RUSSELL
Batch of
| CHIEF OPERATING OFFICER
at Utilities Rgulation and Competition Authority
| Bahamas
As a DBA and COO, I've always been intrigued by Artificial Intelligence and Machine Learning but was put off by the coding requirements. This program is a game-changer, using intuitive tools to make complex Artificial Intelligence concepts accessible. The hands-on approach ensures you can apply what you learn immediately, even without coding skills. The top-notch instructors make learning engaging and practical, and the networking opportunities with peers from diverse industries are invaluable. I highly recommend this program to any executive looking to harness Artificial Intelligence’s power in their business without delving into code. It's an empowering experience that has tremendously enhanced my strategic approach at work.
READ MORE
Kathryn Jones
Batch of
| Site Coordinator
at Atlas Copco
| United States
I am very happy. This whole experience gave me the confidence I needed to explore my field of work and be a good candidate for many positions. Thank you for everything you did to help and for managing all of our doubts and worries.
READ MORE
Xinyi Huang
Batch of
| Partner
at Flamestone Investment Corp.
| Hong Kong
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
Rosie Manfredi
Batch of
| Sr. Manager Product Management, User Experience & Optimization Testing eBusiness
at HD Supply
| United States
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
Christian Ntsiba Gassuet
Batch of
| Data Engineer
at National Grid
| United States
I have over 10 years experience in database design and development and data integration. I took up this program to acquire the knowledge and skills I need to transition my career to ML and AI supported data projects. This program has a solid University level content but accessible to total beginners in the field of ML/AI. The learning material is heavy but the mentorship sessions are a life saver. Case studies and project assignments provides the needed practical skills and knowledge. I did get great hands-on skills by learning and using RapidMiner and Ikigai no-code AI tools which are all widely used enterprise grade software.
READ MORE
Jesus Yustiz
Batch of
| O-RAN Program Manager
at Nokia
| United States
As someone in a management role, I recognized the importance of AI and its potential to develop new tools for leveraging business, projects, teams, and products. I wanted to learn AI in a way that allowed me to practice the concepts without coding skills. The program was intense, engaging, and not overwhelming. The video lectures were informative, and the mentoring sessions were amazing. One key takeaway for me was gaining a general overview of various algorithms and methodologies used in AI. I also discovered the usage of Rapidminer, which was a valuable addition to my learning.
READ MORE
Sharath Chandra, Chindam
Batch of
| Software Developer
at Ford Motor COmpany
| United States
The program has been a rewarding experience, requiring weekend dedication to delve into mathematical foundations through pre-work and detailed video lectures. The program employs drag-and-drop tools like RapidMiner and Ikigai for comprehensive case studies, enhancing understanding through practical projects. Faculty members are approachable. Productive discussions between students and faculty foster inspiration and exchange of ideas. With newfound confidence, learners can explore AI-ML concepts further, supported by career guidance and insightful interviews from faculty.
READ MORE
Venu Janga
Batch of June 2023
| United States
The no-code AI/ML course offered an accessible and empowering introduction to complex technologies, making it easy for me to grasp and apply AI/ML concepts.The course's practical approach and hands-on use cases created a valuable learning experience, enabling me to build real-world AI and ML solutions without extensive coding knowledge.I appreciate the course's structured content and clear explanations, making it an excellent choice for anyone seeking a user-friendly path to harnessing the power of artificial intelligence and machine learning. I love the interactive sessions and office hours with industry experts and professors. Program lead Kamakshi Joshi Is very proactive and remind on assignments very friendly and knowledgeable. Overall great course..
READ MORE
Elena Chico Palazon
Batch of June 2023
| Spain
I felt my enthusiasm grow and grow with each new model, assignment and project. It has been challenging sometimes to gain perspective and allocate time and resources. Being able to fullfil the learning demands of this course have made me more eager to continue learning and to look for opportunities to apply what I learnt. Thank you. I enjoyed every minute!!
READ MORE
Patrick O'connor
Batch of June 2023
| United States
The foundational pre-course learning block was really helpful and worth learning properly: I found it gave me a strong running start for the initial course blocks. The academic quality and program/ administrative quality were both excellent: kudos to both MIT and Great Learning respectively. I found the RapidMiner hands-on experience to be very "tangible" in putting principles into practice. The projects were challenging and interesting; which they account for a relatively small % of the total marks, they provided a disproportionate amount of the learning/ experience valueI think the insight into more advanced capabilities (and tooling) such as Gen AI left me with appetite to learn more in this ML/ AI roadmap
READ MORE
Robert Marcial
Batch of June 2024
| United States
Professor Nitin Sethi is great mentor. Professor Nitin has great handle on the full attributes of Rapid Miner and the installation , manipulation and attributes of the various datasets. In addition, Prof Nitin combines all of the concepts details of Rapid Miner and how they relate to the course module. It has been my honor to be taught by such a great and knowledgeable mentor. Thank you.
READ MORE
Nelson Gonsen
Batch of February 2023
| United States
As a business man it was so hard, but challenging. I recommend that warning new users might be a good idea. Support team is terrific and comprehensive.Learning materials are very good, even the pre work.
READ MORE
Program Fees
*Subject to partner approval based on applicable regions & eligibility.
Benefits of learning from us
- Recorded lectures from MIT Faculty
- Live Personalized Mentorship from leading AI experts
- Comprehensive curriculum and World-class learning material
- 3+ industry-relevant projects and 15+ real-world case studies
- Unique no code approach
- Get personalized assistance with a dedicated Program Manager
Payment and Refund Policy
Candidates can pay the course fee through Credit/Debit Cards and Bank Transfer. Please note that payment is accepted only in US dollars. For further details, please get in touch with your program Advisor.
Refund Policy
If the participant communicates that they will drop the course before the cohort start date, the fee paid will be returned as per the refund policy
mentioned here
Application Process
1
Fill the application form
Register your interest by filling in the
online application form.
2
Application Screening
Your application will be reviewed to determine if it is a fit with the program.
3
Join the program
If selected, you will receive an offer for the upcoming cohort. Secure your seat by paying the fee.
Upcoming Application Deadline
Applications are reviewed on a rolling bases and closed once the requisite number of learners enroll for the upcoming cohort. Apply early to secure your seat.
Deadline: 12th Dec 2024
Apply Now
Frequently Asked Questions
Program Details
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.
Application Process and Eligibility
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
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.
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
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
