This course is designed to provide business leaders with a comprehensive understanding of artificial intelligence and its strategic applications. By the end of this course, participants will be equipped with the knowledge and skills to effectively integrate AI into their organizations, enhancing decision-making and operational efficiency. The objectives of this course are structured to ensure a solid grasp of AI fundamentals, practical applications, and ethical considerations. The specific objectives of the course are as follows:
Registration open for Batch 2
Online
6 Months
3 hours on weekends
IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras
and
eduXLL
INR 60,000 + 18% GST
This course is designed for managers, business leaders, and professionals who want to integrate AI into their organizations to enhance productivity, optimize processes, and stay competitive in the evolving market.
No specific technical background is required, but familiarity with business operations and interest in AI applications will be helpful for participant.
60 Hours
DR. VANDANA SRIVASTAVA
Ph.D. (Jamia Millia Islamia, Delhi)
M.Tech (Computer Applications), IIT- Delhi
M.Sc (Physics), Lucknow University
• What is AI? (Defining AI, Machine Learning, Deep Learning, and Generative AI in a business context)
• Historical evolution of AI and its current state
• Key AI terminology for managers (algorithms, models, data, training, bias, ethics)
• Business Impact of AI
• AI strategy and Vision
• AI systems as Agents
• Searches and their role in AI
• Understanding Uninformed, Heuristic and Adversarial Search
• Understanding different types of data (structured, unstructured, real-time)
• Importance of data quality, privacy, ethics and governance
• Basic Data Analysis and Visualization (Hands-on with Excel & simple dashboards using Power BI or Tableau)
• Introduction to SPSS for Business Insights
• Core Concepts of Machine Learning - Supervised vs. Unsupervised Learning
• Regression (predicting continuous values, e.g., sales forecasting), Classification (categorizing data, e.g., customer churn prediction) and Clustering (identifying natural groupings, e.g., customer segmentation) using easy to use (No code/ Low Code) tools such as SPSS (Note: Focus on interpreting SPSS outputs rather than complex statistical modeling)
• Basic concepts
• Classification
• Artificial Neural Networks
• Learning Association
• Clustering
• Concept
• Applications
• Evolution of Workforce Dynamics
• Impact on Employment