AI & Machine Learning Programs
A specialized track that equips learners to build and apply intelligent systems. Data preprocessing is a critical step across all fields — ML, DL, NLP, and CV — forming the backbone of every successful AI model.
Machine Learning Engineer
Master the technical skills needed to build, deploy, and maintain machine learning systems at scale.
Foundations
- AI concepts & real-world use cases
- Python programming for machine learning
- Introduction to ML using scikit-learn
- Importance of data preprocessing in ML workflows
Core
- Supervised learning: regression & classification
- Feature engineering & model evaluation
- Deep learning fundamentals using TensorFlow
Advanced
- Natural Language Processing (NLP)
- Computer Vision with CNNs
- Deploying ML Models (MLOps)
AI Product Specialist / Strategist
Bridge the gap between technical AI capabilities and business value, leading the strategy and implementation of AI solutions.
Foundations
- AI basics for business professionals
- Ethical considerations & data readiness in AI projects
Core
- Applied AI use cases (e.g., healthcare, retail)
- AI project planning & lifecycle
Capstone
- Design an AI-driven product
- Build a go-to-market strategy