Artificial Intelligence and Machine Learning for Enterprises

Course Objectives

• Understand the fundamental principles of artificial intelligence (AI) and machine learning (ML) and their applications in enterprises.
• Identify use cases and challenges for AI and ML in business contexts.
• Gain familiarity with the tools, techniques, and workflows for implementing AI/ML projects.
• Develop strategic insights to align AI/ML initiatives with enterprise goals.

Course Content

Introduction to AI and ML for Enterprises

• Overview of AI and ML: Definitions, types, and distinctions.
• Evolution of AI and its relevance in enterprise settings.
• Business opportunities and challenges in adopting AI/ML.
• Key ethical considerations and responsible AI principles.

Core Concepts and Tools of AI/ML

• Machine learning basics: Supervised, unsupervised, and reinforcement learning.
• Data and feature engineering: Importance and strategies for preparation.
• Introduction to tools: Overview of popular AI/ML platforms and frameworks (e.g., Python, TensorFlow, Scikit-learn).
• Case studies of AI/ML success stories in enterprises.

AI/ML Project Lifecycle and Applications

• Key stages of AI/ML projects: Problem definition, data collection, model development, and evaluation.
• Use cases in enterprises: Customer insights, predictive analytics, and process optimization.
• Introduction to model deployment: Basics of integrating AI/ML models into enterprise systems.
• Real-world challenges: Scalability, interpretability, and bias mitigation.

Strategic Implementation and Future Trends

• Aligning AI/ML initiatives with business goals: Roadmaps and success metrics.
• Change management for AI/ML adoption in enterprises.
• Emerging trends: Generative AI, AI-augmented decision-making, and edge AI.
• Final group activity: Brainstorming potential AI/ML projects for participants’ organizations.

DATE:
1ST BATCH: 4th – 7th Mar, 2025
2ND BATCH: 12th – 15th Aug, 2025