In our age of information and insights, the success of a business relies on how well we utilize the data in creating algorithms and making predictions. As organizations realize this, no-code approaches to implementing emerging technologies are becoming popular and essential to know. A no-code approach to data science and AI can be a game-changer. MIT Professional Education offers the No Code AI and Machine Learning: Building Data Science Solutions Program to utilize the full power of AI and build intelligent solutions from data without having to write a single line of code.
In this 12-week program, you will learn to use AI and Machine Learning to make data-driven business decisions by understanding the theory and practical applications of supervised and unsupervised learning, neural networks, recommendation engines, computer vision, etc. Leverage the power of AI and data science without writing a single line of code.
Upon successfully completing the program, you will receive a certificate of completion from MIT Professional Education.
Machine learning and AI techniques have generated significant interest across industries. With the emergence of no-code platforms, professionals from diverse sectors can now harness the power of these technologies without prior programming knowledge. These intuitive, interactive user interfaces enable users to efficiently classify information, conduct data analysis, and develop precise predictive models, eliminating the need for complex programming. In this program, you will learn to use different no-code tools to create innovative solutions. It includes modules on Generative AI, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Agentic AI, equipping you with a comprehensive understanding of cutting-edge AI capabilities.
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.
Contact Great Learning for more information at ncai.mit@mygreatlearning.com or call +1 617 860 3529
- Develop the ability to transform complex data into actionable business insights using intuitive, no-code platforms.
- Gain a solid conceptual foundation in supervised and unsupervised learning, recommendation systems, deep learning, and computer vision.
- Gain confidence in rapidly prototyping, testing, and operationalizing machine learning models without coding expertise.
- Gain the practical skills to leverage Generative AI, Prompt Engineering, and Agentic AI for designing intelligent, autonomous workflows.
Program Curriculum
The No-Code AI and Machine Learning Program is a 12-week course that offers a comprehensive learning experience. Esteemed MIT Faculty lead the program and incorporate a blended learning approach with recorded lectures, real-life case studies, hands-on projects, interactive quizzes, mentor-led sessions, and engaging webinars.
These 12 weeks will be distributed in the following manner:
Module 1: Introduction to AI Landscape
Module 2: Data Exploration - Structured Data, Networks, and Graphical Models
Module 3: Prediction Methods - Regression
Module 4: Decision Systems
Module 5: Data Exploration - Unstructured Data
Module 6: Recommendation Systems
Module 7: Data Exploration - Temporal Data
Module 8: Prediction Methods - Deep Learning and Neural Networks
Module 9: Computer Vision Methods
Module 10: Workflows and Deployment
Week 1
Module 1: Introduction to the AI and Generative AI Landscape
This module explores AI’s history, role in organizations, data operations, and strategic approaches to building AI products to drive innovation and efficiency. Here’s what it covers:
- AI and generative AI landscape: history and landscape
- Organizations, people, and data
- Data operations in various organizations
- Strategy for building AI products
Week 2
Module 2: Data Exploration - Structured Data
This module gives practical insights into analyzing and interpreting structured data using advanced techniques. Here’s what it covers:
- Clustering (K-means clustering, K-medoids, Gaussian mixture)
- Dimensionality reduction techniques (PCA, t-SNE)
Week 3
Module 3: Prediction Methods – Regression
This module explores regression techniques like Linear Regression, K-Fold Cross-Validation, Bootstrapping, and LOOCV to build accurate predictive models. Here’s what it covers:
- Linear regression
- Assumptions of Linear Regression
- K-fold cross-validation
- Bootstrapping
- Leave-one-out cross-validation (LOOCV)
Week 4
Module 4: Decision Systems
This module dives into classification techniques like Decision Trees and Random Forests to make accurate predictions from categorical data. Here’s what it covers:
- Decision tree
- Bagging
- Random forests
Week 5
Project Week - Machine Learning Classification
Week 6
Module 5: Recommendation Systems
This module explores how to develop powerful tools that personalize user experiences, a key asset in today's data-driven landscape. Here’s what it covers:
- Recommendation systems: problem statements and solutions
- Clustering-based recommendation systems
- Collaborative filtering
- Rank-based and content-based techniques
Week 7
Module 6: Prediction Methods – Neural Networks
This module introduces deep learning, mastering core concepts, neural network building blocks, and training techniques. Here’s what it covers:
- Introduction to deep learning
- Building blocks of neural networks
- Training neural networks
- Digit recognition case study
Week 8
Module 7: Computer Vision Methods
This module dives into the fascinating world of machines that can see and interpret visual data. It explores CNN building blocks, training techniques, and practical applications like image detection and object recognition. Here’s what it covers:
- 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
Module 8: Generative AI Foundations
This module explores the foundational concepts of Generative AI, beginning with exploring its origins and the underlying principles behind generating new data. Here’s what it covers:
- 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
Module 9: Business Applications of Generative AI
This module dives into the business applications of Generative AI, including Retrieval-Augmented Generation (RAG) for improving response relevance and Agentic AI for autonomous decision-making. Here’s what it covers:
- Natural language tasks with generative AI
- Summarization, classification, and generation
- Retrieval-augmented generation (RAG)
- Agentic AI
Week 12
Module 10: Ethical and Responsible AI
This module explores the ethical and responsible development of AI systems, from the AI lifecycle to addressing bias, causality, and privacy concerns. Here’s what it covers:
- 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
