Programme Content
The session is designed to set context and align expectations for the learning journey ahead. Learners are introduced to the program structure, learning flow, delivery format, and key milestones, along with guidance on how to make the most of live sessions, hands-on components, and assessments. This session also helps learners understand how the program’s components connect, ensuring a smooth, well-paced overall experience.
This module is designed to help learners strengthen their Python fundamentals while becoming familiar with AI-assisted coding practices. It focuses on building programming confidence, reinforcing core concepts, and introducing how AI can support writing, reviewing, and optimizing code. This Generative Artificial Intelligence and Machine Learning course module ensures learners are well prepared for the subsequent applied data science and machine learning modules.
This module focuses on building practical data science skills using Python, with an emphasis on understanding data and applying statistical concepts. Learners develop the ability to work with real-world datasets by combining programming, statistics, and visualization techniques. The module is designed to help learners move from raw data to meaningful insights that support analytical and business decisions
This module provides a strong foundation in machine learning by guiding learners through the design, training, evaluation, and improvement of models. It covers both supervised and unsupervised learning techniques, helping identify when to use each approach and how to evaluate their performance. Through structured concepts and applied examples, learners gain confidence in moving from data to predictive and descriptive insights
This module introduces deep learning concepts and techniques that power modern AI systems. It builds a clear understanding of how neural networks work, how deep learning models are trained and optimized, and how widely used architectures are applied to real-world problems. Learners gain hands-on exposure to industry-standard frameworks while developing the ability to reason about model performance, limitations, and practical use cases.
This module introduces the fundamentals of generative AI and how these systems are used in real-world workflows. It focuses on building conceptual clarity about how generative models work, how their outputs are shaped by prompts, and how specific tools are used in everyday professional tasks. This Generative AI and Machine Learning online course module is designed to help learners use generative AI thoughtfully and effectively, while remaining aware of its limitations and risks.
This module deepens learners understanding of how modern generative AI systems are designed, evaluated, and adapted for various applications. It focuses on the architectures and techniques behind large language models, advanced prompting strategies, and retrieval-augmented generation, along with practical frameworks for building AI-powered applications. Learners also gain exposure to methods for fine-tuning and evaluation practices for assessing and improving model performance
The program concludes with a capstone project that brings together the concepts and skills learned across the learning path. Guided by expert mentors, learners work on a real-world business problem and apply AI and generative AI concepts to design an end-to-end solution. The capstone helps reinforce both technical understanding and problem-solving skills, while resulting in a portfolio-ready project that demonstrates practical experience and job readiness to prospective employers
Build a strong foundation in Artificial Intelligence, Data Science, and Machine Learning through faculty-led sessions by IIT Kanpur experts. Explore AI fundamentals, machine learning concepts, supervised learning, linear models, neural networks, and data science principles. Hands-on programming exercises reinforce concepts and enable practical application of modern AI techniques.
Advance your machine learning expertise through IIT Kanpur faculty-led sessions covering advanced AI concepts and real-world applications. Explore Association Rule Mining, Decision Trees, Probabilistic Machine Learning, Maximum Likelihood Estimation, and Natural Language Processing. Gain practical insights to apply advanced machine learning techniques to complex business and research challenges.
AI Impact Studio is an exclusive workshop series keeping learners ahead of the AI curve — live, expert-led sessions on Generative AI, Agentic AI, and automation. Get hands-on with tools like Claude, n8n, OpenClaw, LangChain in the Builder track, or apply AI to decisions, productivity, and growth in the Business track. Real labs, real use cases, and career-ready insights — value that goes far beyond the core curriculum.
This masterclass introduces learners to emerging concepts in agentic AI and the design of agents that can plan, reason, and act across multi-step workflows. It focuses on how agentic systems operate, the role of autonomy in modern AI applications, and key deployment considerations. Through interactive discussions and examples, learners gain exposure to the principles behind building intelligent autonomous or semi-autonomous AI systems
This elective introduces version control concepts and collaborative workflows using Git and GitHub. It focuses on how code is tracked, shared, reviewed, and maintained across teams, with an emphasis on actual development practices. Learners gain practical experience with local and remote repositories, branch management, conflict resolution, and GitHub Actions to automate workflows
This elective introduces the foundations of natural language processing and speech analytics, covering how text and audio data are represented, processed, and analyzed using machine learning and deep learning techniques. The module also extends into speech recognition by introducing audio analytics, signal-processing fundamentals, feature-extraction techniques, and deep learning approaches for speech and audio generation
This elective introduces the core ideas behind reinforcement learning and how agents learn through interaction with an environment. It covers key concepts such as rewards, policies, value functions, and decision-making models, including Markov decision processes. Learners are exposed to common solution methods, including dynamic programming, Monte Carlo methods, temporal difference learning, and Q-learning, before progressing to deep reinforcement learning approaches
This elective introduces fundamental concepts related to artificial intelligence (AI) and the services in Microsoft Azure that can be used to create AI solutions. It teaches a mix of AI concepts and technology skills that are considered foundational to a successful career implementing AI solutions on Microsoft Azure.
