Course Outline
1 - AI Foundations & Data Analysis (6.5 hours)
- Introduction to AI ecosystem, domains, and emerging trends
- Data types, preprocessing & exploratory data analysis
- Feature engineering & visualization
- Hands on: Data exploration using Python tools
2 - Machine Learning (6.5 hours)
- Supervised & unsupervised learning
- Regression, classification, clustering methods
- Model evaluation metrics
- Hands on: Building ML models
3 - Deep Learning & NLP (6.5 hours)
- Neural networks, activation functions & optimization
- CNN architectures for vision tasks
- Foundations of NLP (tokenization, embeddings, transformers)
- Hands on: Building simple NN & NLP models
4 - Computer Vision, Robotics, Governance & Risk (6.5 hours)
- Computer Vision workflows & applications
- Robotics & expert systems
- AI governance, ethics, privacy, transparency
- Responsible AI frameworks & risk management
5 - Exam Preparation + Certification Exam (6 hours)
- Review exercises and mock questions
- Scenario based evaluation
- Official certification exam
Target Audience
This programme is intended for AI professionals, developers, data scientists, machine learning engineers, IT managers, AI strategists, project managers, governance and compliance professionals, and executives involved in Artificial Intelligence initiatives and digital transformation projects.