Build Practical Expertise in AI, ML, GenAI, and Agentic AI

Establish the coding and conceptual foundations needed for the program.

Use AI coding tools to accelerate Python development while evaluating generated code critically.

Apply inferential statistics and AI tools to draw defensible conclusions from sample data.

Apply dimensionality reduction and clustering techniques to uncover patterns in high-dimensional data.

Build and rigorously evaluate supervised Machine Learning models for regression and classification.

A dedicated week of refresher sessions to help you catch up on coursework and consolidate your learning.

Apply tree-based models and time series methods to solve classification, regression, and forecasting problems.

Build and apply neural network architectures, including CNNs and transfer learning pipelines.

Build production-ready recommendation systems that handle sparse and time-varying data at scale.

A dedicated week of refresher sessions to help you catch up on coursework and consolidate your learning.

Develop an end-to-end solution by selecting a problem statement from a chosen domain and applying appropriate data science and AI techniques.

Understand the architecture of autonomous AI agents and build functional single-agent systems.

Design multi-agent systems and evaluate their performance using production-grade metrics.

Design and deliver an end-to-end AI solution for a selected problem statement, integrating concepts and techniques from across the program.

Understand and apply ethical principles across the AI lifecycle to design fair, transparent, and responsible AI systems.

This module is designed to build practical capability in applying Artificial Intelligence and Data Science using the Claude ecosystem in real-world contexts. Learners build the ability to design, execute, and evaluate AI-driven workflows for real-world applications, supported by ~5 hours of structured learning.

This masterclass covers the Anthropic AI landscape, exploring Claude models, Constitutional AI, and key safety and alignment principles. Learners will apply effective prompting, use the Claude API for tasks and integrations, generate structured outputs, build simple applications, critically compare Claude with other AI models, and evaluate ethical considerations for deploying AI systems.

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

Build an end-to-end solution by selecting a problem from retail analytics, HR analytics, forecasting, computer vision, or recommendation systems. Work on real-world datasets to apply exploratory data analysis, Machine Learning, deep learning, and recommendation system techniques to solve a chosen business problem. Skills you will learn: EDA, Machine Learning, Deep Learning, Recommendation Systems, Predictive Modeling

Design and deliver an end-to-end AI solution on a problem statement of your choice, drawing from any topic covered in the program. Work on real-world challenges across data science, Machine Learning, deep learning, recommendation systems, and generative and Agentic AI workflows to build scalable, production-ready solutions. Skills you will learn: End-to-End AI Systems, Data Science, Machine Learning, Deep Learning, Generative AI, Agentic AI