This 6-hour intensive syllabus is designed to take participants from the foundational
mechanics of Natural Language Processing (NLP) to intermediate and advanced applications of Generative AI. By consolidating key topics into focused, one-hour modules,
learners will explore the LLM ecosystem, Retrieval-Augmented Generation (RAG), AI
Agents, Multimodal Vision, and Parameter-Efficient Fine-Tuning (PEFT)
Course Syllabus
6 modules • 15 topics
1
Module 1: Foundations of Generative AI & NLP (1 Hour)
2 topics
Gateway to Generative AI: Exploring New Frontiers
Decoding Language: The Mechanics of NLP and LLMs
2
Module 2: The LLM Ecosystem &Tooling (1 Hour)
3 topics
Open Source AI Powerhouse: Exploring Hugging face
Harnessing the LLM APIs and Open AI Ecosystem
AI Tools: Building, Moderating, and Leveraging AI
3
Module 3: Context-Aware Systems & Lang Chain (1 Hour)
2 topics
Intro to Lang chain: Enhancing LLMs with Contextual Memory
Dynamic AI with RAG: Building Context-Aware Systems
4
Module 4: Advanced RAG & Vector Stores (1 Hour)
2 topics
Understanding Embeddings
Advanced RAG Techniques and Vector Stores
5
Module 5: The Age of AI Agents (1 Hour)
3 topics
The Final Level of Gen AI: Agents
Agents Evolution: Connect to the Web and Integrate Tools
Data Scientist and Analytics professional with experience across industrial, hospitality, banking, manufacturing, and AI applications. Developed predictive models for SABIC, achieving 80% reduction in NOx forecast errors and consistently maintaining MAPE below 6%. Improved Taj Hotels’ cross-selling recommendations, increasing menu sales by 25%, and supported fraud prevention at RBL Bank. Built advanced SARIMAX forecasting solutions for Americold and designed an Agentic AI Flow Builder using microservices, RAG, and intent classification. Additional expertise includes Computer Vision, recommendation systems, LLMs, chatbots, and question answering. Skilled in Python, SQL, AWS, SageMaker, Scikit-Learn, Streamlit, and advanced machine learning.
