Curriculum
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.
- 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.
Week 1
Module 1: Introduction to the AI Landscape
To offer a general overview of the four blocks upon which this No Code AI and Machine Learning Program is focused.
- Understanding the data: What is it telling us?
- Prediction: What is going to happen?
- Decision Making: What should we do?
- Causal Inference: Did it work?
Week 2
Module 2: Data Exploration - Structured Data
To learn the basic principles of applying data exploration techniques, such as dimensionality projection and clustering on structured data.
- Asking the right questions to understand the data.
- Understanding how data visualization makes data clearer.
- Performing Exploratory Data Analysis using PCA.
- Clustering the data through K-means & DBSCAN clustering.
- Evaluating the quality of clusters obtained.
Week 3
Module 3: Prediction Methods - Regression
To understand the concept of linear regression and how it can be used with historical data to build models that can predict future outcomes.
- The idea of regression and predicting a continuous output.
- How do you build a model that best fits your data?
- How do you quantify the degree of uncertainty?
- What do you do when you don’t have enough data?
- What lies beyond linear regression?
Week 4
Module 4: Decision Systems
To understand the concept of classification and understand how tree-based models achieve prediction of outcomes that fall into two or more categories.
- Understand the Decision Tree model and the mechanics behind its predictions.
- Learn to evaluate the performance of classification models.
- Understand the concepts of Ensemble Learning and Bagging.
- Learn how Random Forests aggregate the predictions of multiple Decision Trees.
Week 6
Module 5: Data Exploration - Unstructured Data
To understand the concept of Natural Language Processing and how natural language represents an example of unstructured data, the business applications for this kind of data analysis, and how data exploration and prediction are performed on natural language data.
- Understand the concept of unstructured data, and how natural language is an example.
- Understand the business applications for Natural Language Processing.
- Learn the techniques and methods to analyze text data.
- Apply the knowledge gained towards the business use case of sentiment analysis.
Week 7
Module 6: Recommendation Systems
Week 8
Module 7: Data Exploration - Temporal Data
To understand the critical concept of temporal data, and its differences from structured and unstructured data, the idea behind Time Series Forecasting and the preprocessing required to obtain stationarity in Time Series.
- Understand temporal data and how it represents a different data modality.
- Understand the idea behind Time Series forecasting
- Learn about the concept of Stationary Time Series, testing for stationarity and conversion techniques to transform non-stationary time series into stationary.
Week 10
Module 8: Prediction Methods - Neural Networks
To understand the ideas behind Neural Networks, their introduction of non-linearities into the encoding and predictive process through a hierarchical structure, and the various steps involved in their forward propagation and back propagation cycle to minimize prediction error.
- Understand the key concepts involved in Neural Networks.
- Learn about the encoding process taking place in the neural network layers, and how non-linearities are introduced.
- Understand how the forward propagation happens through the layered architecture of neural networks and how the first prediction is achieved.
- Learn about the cost function used to evaluate the neural network’s performance, and how gradient descent is used in a backpropagation cycle to minimize error.
- Understand the critical optimization techniques used in gradient descent
Week 11
Module 9: Computer Vision Methods
To understand how images represent a spatial form of unstructured data and hence, a different data modality, how the Convolutional Neural Network (CNN) structure achieves generalized encoding abilities from image data and acquire an understanding of what CNNs learn.
- Learn about spatial concepts of images such as locality and translation invariance.
- Understand the working of filters and convolutions, and how they achieve feature extraction to generate encodings.
- Learn about how these concepts are used in the structure of Convolutional Neural Networks (CNNs) and understand what CNNs actually learn from image data.
Week 12
Module 10: Workflows and Deployment
To obtain additional perspective on how the same takeaways from the conceptual modules discussed prior have been applied in various business scenarios and problem statements by industry leaders who have achieved success in practical applications of Data Science and AI.
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.
Entertainment
Movie Lens Data Exploration
The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. In this project, we will perform exploratory data analysis to understand the popularity trends of movie genres and derive patterns in movie viewership.
Tools & Concepts: Python, SQL, Pandas, NumPy, Data Summary and Description
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Insurance
Insurance Claim Prediction
A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In this project, we will build a regression model to predict the cost of insurance claims using user information on age, gender, bmi, blood pressure, health conditions, as well as insurance claim details.
Tools & Concepts: Linear Regression, Model evaluation, Tuning, Exploratory Data Analysis, Python
Learn more
Telecom
Network Congestion Type Prediction
The telecom industry is faced by a common challenge of network congestions due to various factors. To solve this problem, we will use data usage information of mobile phones from various telecom companies and find out a relation between the features of the mobile phone service provider (eg: bytes consumed through various services, etc.) and types of network congestion.
Tools & Concepts: Machine Learning, Classification, Model Tuning, Cross Validation
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Sales & Marketing
Forecasting Monthly Sales of French Champagne
Being able to make accurate predictions of future revenue can be hugely important for businesses. This project will focus on forecast the next monthly revenue of a french chamapagne brand, which will inform the decision-making process across all areas of the business, from purchasing decisions and marketing activity to staffing levels.
Tools & Concepts: Time Series Analysis, Predictive Modelling, Python for Time Series
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Healthcare
COVID-19 Global Forecasting
The capstone project is a focused approach to attempt a real-life challenge with the learnings from the program. In this project, you will use a combination of various datasets and models to forecast and predict daily cases and deaths. You will also create various plots to gain insights and showcase your results. the data has been collected from different sources like WHO, WorldoMeters, 1Point3Arces and many more to understand various aspects and build relevant features to use in models.
Tools & Concepts: Time Series Analysis, Python
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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
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Object Detection
Face Mask Segmentation
Predict and apply masks over the faces detected in images using Convoluted Neural Networks and image recognition algorithms. The goal of the project is to build a system that acts as a face detector to locate the position of a face in an image and apply a segmentation mask on the face. For this, we will utilise data from over 409 images and 1000 faces from the WIDER FACE dataset.
Tools & Concepts: Computer Vision, CNN, Transfer Learning, Object detection, Segmentation, TensorFlow
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News & Media
Sarcasm Detection
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision, but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs. The dataset is collected from two news websites, theonion.com and huffingtonpost.com.
Tools & Concepts: LSTM, Classification, GloVe, TensorFlow
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MIT Faculty
Learn from world-renowned MIT faculty in the field of Data Science, Machine Learning, and Artificial Intelligence.
Program Faculty
Munther Dahleh
Program Faculty Director
MIT Institute for Data, Systems, and Society (IDSS)
Stefanie Jegelka
X-Consortium Career Development Associate Professor
Electrical Engineering Computer Science (EECS) at MIT
Devavrat Shah
Director
Statistics and Data Science Center (SDSC)
John N. Tsitsiklis
Clarence J. Lebel Professor
Dept. of Electrical Engineering & Computer Science (EECS) at MIT
Caroline Uhler
Henry L. & Grace Doherty Associate Professor
Institute for Data, Systems and Society (IDSS)
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 sessions crafted by MIT Faculty
- Flexible self-paced learning with recorded lectures
- Designed and delivered by MIT faculty
- Learn to take AI-backed decisions for business growth
Personalised Mentorship and Support
- Live mentorship by industry experts
- Interaction with like-minded peers from diverse backgrounds and geographies
- Dedicated Program Manager for program support
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
Program Fees
No Code AI and Machine Learning Program
USD 2500
- 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
- A dedicated program manager for program support
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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 in full, minus a $300 USD administrative fee. Cancellation requests and reimbursements will be carried out under the following criteria.
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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: 9th Feb 2023
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 6-8 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 sheet after completion of the program?
No, the No Code AI and Machine Learning Program is an online professional certification 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, therefore, there are no grade sheets or transcripts for this program by the university. 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.
Will I receive a transcript or grade after completion of the program?
No, the No Code AI and Machine Learning Program is an online professional certification 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.
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, Ikigai, and Lobe.ai, which you can use to implement business solutions to various data modalities and problem statement paradigms in Artificial Intelligence and Machine Learning.
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.
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 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.
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.
Application Process and Eligibility
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
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.
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?
If you communicate that you will drop the course before the cohort start date, the cost will be returned in full, minus a $300 USD administrative fee. Cancellation requests and reimbursements will be carried out under the following criteria.
- Communication period: before the initial start date of the program cohort. Requests submitted after this date will not be eligible for reimbursement.
- Means of communication: Requests must be submitted by email to the following address: ncai.mit@mygreatlearning.com Participants will not be eligible for reimbursement after the initial start date of the program cohort.
- Exceptions: refunds due to medical reasons or other justified reasons, including force majeure, during the communication and payment period (21 days from the program’s cohort start date) will be exempt from paying the administrative fee of $300 USD provided that the request is carried out within the communication period, sent to the email address indicated above, and accompanied by corresponding documentation (medical, police, or psychiatric reports, etc.). Once the request has been received, the academic committee will review it to determine whether or not it is admissible.
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.
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