Day 1:
Introduction to Artificial Intelligence
Understanding AI and its Impact on Society and Business
The History and Evolution of Artificial Intelligence
Overview of Machine Learning, Deep Learning, and Neural Networks
Types of AI Systems: Supervised, Unsupervised, and Reinforcement Learning
Day 2:
Key AI Technologies and Tools
Introduction to Python and R for AI Development
Overview of Machine Learning Frameworks: TensorFlow, PyTorch, and Scikit-Learn
Natural Language Processing (NLP) and its Applications
Computer Vision and Image Recognition in AI
Day 3:
Practical Applications of AI
AI in Healthcare: Diagnosis and Treatment Optimization
AI in Finance: Risk Management and Fraud Detection
AI in Marketing: Personalization and Customer Experience
Case Study: AI in Manufacturing and Automation
Day 4:
Ethical and Social Implications of AI
Addressing Bias and Fairness in AI Models
AI and Data Privacy: Challenges and Solutions
The Future of Work: AI’s Impact on Employment and Skill Development
Ethical Considerations: Accountability and Transparency in AI
Day 5:
Implementing AI in Business and Industry
Building a Business Case for AI Adoption
AI Integration: From Prototype to Production
Monitoring and Evaluating AI Systems in Practice
Case Study: Real-World AI Projects and Success Stories
