Online Post Graduate Diploma in AI-ML for Managers

• Professionals from consulting, operations, marketing, finance, or business roles seeking to enter enterprise AI/ML domains

• Want to build strong foundations in AI/ML concepts without needing heavy coding depth

• Aspiring to move into managerial, cross-functional, or AI strategy roles.

• Principles of human-computer interaction in data visualization

• Visual encoding for effective data communication

• Handling information overload using visual design

• Techniques: heat maps, infographics, and dashboards

• Multidimensional data visualization and graphical perception

• Mapping, cartography, and text visualization

• Visualization for improved comprehension and decision-making

• Internet as a research and data collection medium

• Research design and sampling techniques

• Online surveys and non-reactive data collection

• Virtual ethnography and online focus groups

• Blogs, videos, and secondary qualitative data sources

• Data analysis approaches and research tools

• Use of Generative Al for research and content creation

• Prompt engineering for non-technical users

• Fundamentals of machine intelligence and learning

• Linear learning models

• Artificial Neural Networks (single-layer and multi-layer)

• Backpropagation and learning variants

• Support Vector Machines (classification and regression)

• Learning theory and model complexity (VC dimension, PAC learning)

• Unsupervised learning: PCA and KPCA

• Clustering techniques

• Feature selection methods

• Introduction to semi-supervised learning

• Fundamentals of Database Management Systems (DBMS)

• Hierarchical and multidimensional data modeling

• Data warehouse design and ETL processes

• SQL for data warehousing

• OLAP and OLTP concepts

• Data warehousing risks and management issues

• Designing and expanding data warehouse applications

• Nonlinear dimensionality reduction techniques

• Maximum entropy and exponential family models

• Graphical models

• Computational learning theory

• Structured Support Vector Machines

• Feature and kernel selection methods

• Meta-learning and multi-task learning

• Semi-supervised and reinforcement learning

• Approximate inference methods

• Clustering and boosting techniques

• Overview of enterprise-level AI/ML systems

• AI/ML use cases across enterprises

• Managerial and operational challenges of AI/ML systems

• Economic assessment of AI/ML projects

• Effort estimation, pricing, and costing models

• Responsible Al: fairness, ethics, transparency, accountability

• Governance frameworks for AI/ML systems

• Risks, unintended consequences, and policy interventions

• Emerging research and practice in IT management

• Contemporary and evolving technology topics

• Industry-relevant and research-driven themes

• How firms are using NLP for impacting business growth

• Digital content management using NLP

• Social media analytics using NLP and Network Science

• Applications of Generative Artificial Intelligence

• Prompt engineering for leveraging Large Language Models

• Advanced concepts and applications

• Covers emerging topics and advanced concepts in modern information processing

• Integrates research insights with practical, real-world applications

• Builds conceptual depth and analytical understanding of evolving information systems

• Deep Learning concepts and applications

• Current research and development challenges

• Emerging trends in ML and AI

• Focuses on advanced and specialized topics in Machine Learning and Deep Learning

• Explores current research challenges and industry-relevant ML developments

• Strengthens applied and theoretical competence in modern AI/ML systems

• Capstone Project in Artificial Intelligence and Machine Learning - Hands-on project undertaken in groups of 4 members where students learn to solve complex problems by using advanced machine learning algorithms on large complex datasets.

This programme is ideal for working professionals, managers, and leaders with:

• Graduates in any discipline with minimum 50% marks or 5 CGPA

• Experience in roles such as IT, data, analytics, product, consulting, operations, or digital transformation

• It is not meant for fresh graduates, but for professionals looking to advance into AI-driven leadership roles.

Yes. This AI and ML course for managers is specifically structured for non-coders and business leaders. You will learn AI concepts, ML systems, and data science fundamentals and how to lead AI projects, evaluate AI use cases, and make data-driven decisions. No prior coding or advanced math background is required.

The curriculum spans AI, machine learning, and data science across 2 semesters, 11 courses and 21 credits including:

• Data visualization and research methods

• Machine learning and advanced machine learning

• Enterprise AI/ML systems and governance

• AI strategy, risk, ethics, and deployment

• Capstone project using real business problems

This makes it a complete AI and data science course for professionals.

Yes. The programme includes Python-based tools and ML workflows, along with platforms like PyTorch, NumPy, scikit-learn, and visualization tools. The focus is on practical understanding and application, not just coding depth, making it ideal for managers who want to work with AI teams effectively.

• Duration: 12 months (2 semesters)

• Format: Live online evening classes

• Weekly effort: ~10–12 hours

The programme fee is ₹5,08,475 + GST, with flexible installment options available.

Given its credit-bearing IIT Delhi credential, enterprise-focused curriculum, and affiliate alumni status, it offers strong ROI for professionals aiming for AI-driven leadership roles.

Unlike generic certifications, this is a Credit-bearing Online Post Graduate Diploma from IIT Delhi designed for managers and leaders, not freshers. This online diploma is focused on enterprise AI systems, governance, and strategy which includes an IIT Delhi Affiliate Alumni status. It’s closer to a managerial AI master’s-level experience than a short-term course.