Course Content

• Introduction to course goals and the balance between tech and strategy.
• Fundamentals of AI, ML, deep learning, and data visualization.
• Overview of key statistical concepts for data-driven decisions.
• Summary of regression, time series, and tree-based models in business.
• Best practices in data governance, quality, and security.
• Overview of India’s AI ecosystem, strategy, startups, and investments.
• Data lifecycle, big data, and cloud strategies for scalable AI.
• Understanding structured and unstructured datasets.
• Intro to ML Ops: model deployment, monitoring, and improvement.
• Basics of model maintenance and AI cybersecurity.
• AI applications in finance, marketing, sales, and supply chain.
• Interactive session on aligning technical tools with business strategy.
• Aligning AI initiatives with strategy, budgeting, ROI, and build vs. buy decisions.
• Evaluating high-performance computing needs.
• Best practices for building and leading highperformance AI teams.
• Strategies for workforce development, reskilling, and fostering innovation.
• Industry-specific breakout sessions with tailored insights.
• Structured Q&A sessions.
• Capstone Projects & Use Case Deep-Dives: Collaborative workshops exploring real-world examples and industry-specific topics like Industry 4.0, IoT, time series models, and user segmentation.
• Strategic Integration, Governance, and the Future of Generative AI Morning Strategies for integrating AI into business operations and long-term planning.
• Overview of AI governance, risk management, and ethical considerations.
• Understanding generative AI’s evolution and its industry impact.
• Exploring opportunities and challenges of generative AI.
• Personalized sessions to develop strategic AI roadmaps combining technical and leadership insights. Interactive expert panel discussing challenges and opportunities in AI transformation