What you'll learn
- Explain core ML concepts that underpin Generative AI
- Compare foundation models, LLM architectures, and transformer mechanics
- Design effective prompts, embeddings, and RAG pipelines
- Integrate vector databases, tool calling, memory, and agents
- Fine-tune and evaluate models while applying safety guard-rails
- Architect, operate, and govern GenAI systems in enterprise settings
- Produce AI-native product designs with ethical and privacy considerations
- Prototype multimodal and future-oriented GenAI applications
Curriculum
Module 1: AI & ML Foundations for GenAI4 topics
- Discriminative vs. Generative Models
- Probability Basics for GenAI
- Autoencoders
- Quiz: Module 1: AI & ML Foundations for GenAI
Module 2: Foundation Models Landscape4 topics
- Foundation Model Definition
- Scaling Laws & Training Objectives
- Open-Source Model Zoo
- Quiz: Module 2: Foundation Models Landscape
Module 3: Transformers & Attention Mechanics4 topics
- Self-Attention
- Positional Encoding
- Encoder vs. Decoder
- Quiz: Module 3: Transformers & Attention Mechanics
About this course
A 12-week, 60-hour program that builds solid foundations in Generative AI and Large-Language-Model engineering, then guides learners to design, evaluate, and deploy enterprise-grade GenAI solutions.
Prerequisites
- •Proficiency in Python
- •Basic linear algebra & probability
- •Familiarity with REST APIs and Git
- •Introductory software-engineering coursework