• AI/ML Engineers & Data Scientists
• Software Developers working with LLMs
• Professional interested in AI-powered automation
• Researcher & innovators in the AI field

• Python Programming experience and libraries such as NumPy & Pandas
• Conceptual understanding of machine learning concepts.
• Familiarity with neural networks and transformers (helpful but not mandatory)

Course Outline:

1. Introduction to Open-Source & Frontier models
   1. Overview of key open-source LLMs (e.g. Gemma, Llama, Phi, Mistral, etc.)
   2. Understanding frontier (closed source) LLMs (e.g. GPT, Claude, Gemini, etc.)
   3. Tokenization and output formatting (JSON, Markdown) using HuggingFace
   4. Lab: Build first LLM powered application
2. Building a Multi-Modal Chatbot
   1. Working with Claude, Gemini, and OpenAI APIs
   2. Streaming responses for real-time interactions
   3. Building a chatbot using Gradio and Python
   4. Lab: Develop a multi-modal chatbot integrating different models
3. Working with Open-Source Models & HuggingFace
   1. Exploring HuggingFace models and datasets
   2. Building transformer pipelines for NLP tasks (sentiment analysis, translation, etc)
   3. Tokenization with AutoTokenizer for different LLM models
   4. Introduction to Quantization (reducing weights from 32-bit to 8-bit)
   5. Lab: Experiment with pipelines, tokenizers and quantization
4. Using RAG to build advanced solutions with LangChain
   1. Understanding Retrieval Augmented Generation (RAG)
   2. Chunking optimization for better retrieval performance
   3. Working with Vector Databases (FAISS, Chroma, Pinecone)
   4. Lab: Build RAG pipeline application
5. Curating and working with HuggingFace datasets
   1. Using HuggingFace datasets to curate own data
   2. Predict product prices with traditional Machine Learning and NLP on dataset
   3. Use frontier LLM and prompt engineering to predict the prices
   4. Lab: Develop data curation & price prediction pipeline
6. Fine Tuning LLMs with LoRA/QLoRA
   1. Understanding Transfer Learning & Fine-tuning in LLMs
   2. Modifying neural network layers for fine-tuning
   3. Exploring LoRA and QLoRA hyperparameters for efficient fine-tuning
   4. Using HuggingFace PEFT(Parameter Efficient Fine-Tuning)
   5. Lab: Fine Tuning Frontier LLM using LoRA/QLoRA
7. Agentic AI: Multi agent autonomous system
   1. Understanding Agent AI
   2. Building Agentic workflow architecture
   3. Understanding Agentic AI
   4. Capstone Project: Develop an AI-powered price prediction system

Training material provided: Yes (Digital format)

Hands-on Lab: Use Google’s Colab to perform the hands-on labs, also will provide instructions to run on local machine