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