Hands-On Generative Artificial Intelligence

Getting Started

In Google Colab:

from google.colab import drive
drive.mount('/content/drive')

Then CD into the Colab Notebooks:

!cd /content/drive/MyDrive/Colab\ Notebooks

Clone this repo:

!git clone https://github.com/tatwan/handson-genai.git

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Course Overview

This intensive three-day training course combines theory with extensive hands-on practice to teach participants how to build production-ready generative AI applications using state-of-the-art models and techniques.

Designed for developers, data engineers/analysts, and tech product owners, this course covers the complete GenAI lifecycle from foundations to deployment, including modern topics like diffusion models, multimodal AI, RAG systems, AI agents, and production optimization.

Course Duration: 3 Days
Last Updated: February 23, 2026
Total Labs: 32 hands-on notebooks

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Course Modules

📁 Module Structure

handson-genai/
├── Module_01_ML_Foundations/      # Day 1
│   └── 01_intro_to_ml_concepts.ipynb
├── Module_02_Deep_Learning/       # Day 1
│   ├── 01_neural_network_basics.ipynb
│   └── 02_image_classification_pytorch.ipynb
├── Module_03_Generative_AI/       # Day 1
│   ├── 01_intro_to_generative_ai.ipynb
│   ├── 02_autoencoders.ipynb
│   └── 03_diffusion_models.ipynb          
├── Module_04_NLP/                 # Day 2
│   ├── 01_intro_to_nlp.ipynb
│   ├── 02_tokenization.ipynb
│   └── 03_embeddings.ipynb
├── Module_05_LLMs/                # Day 2
│   ├── 01_openai_ollama.ipynb
│   ├── 02_huggingface_tour.ipynb
│   ├── 03_bert_gpt.ipynb
│   ├── 04_gradio_ui.ipynb
│   └── 05_multimodal_models.ipynb         
├── Module_06_Prompting/           # Day 2
│   ├── 01_prompting_techniques.ipynb
│   ├── 02_function_calling.ipynb
│   ├── 03_function_calling_langchain.ipynb
│   └── 04_react_agent.ipynb
├── Module_07_RAG/                 # Day 3
│   ├── 01_rag_langchain.ipynb
│   ├── 02_rag_llamaindex.ipynb
│   └── 03_rag_evaluation.ipynb
├── Module_08_Fine_Tuning/         # Day 3
│   ├── 01_transfer_learning.ipynb
│   ├── 02_sentiment_analysis.ipynb
│   ├── 02_fine_tuning_openai.ipynb
│   ├── 03_summarization.ipynb
│   ├── 04_sampling_techniques.ipynb
│   └── 05_Fine_Tuning_LLM_Healthcare.ipynb
├── Module_09_Optimization/        # Day 3 
│   ├── 01_intro_to_optimization.ipynb
│   ├── 02_knowledge_distillation.ipynb
│   ├── 03_pruning.ipynb
│   ├── 04_quantization.ipynb
│   └── 05_benchmarking.ipynb
└── Module_10_Capstone/            # Day 3
    └── capstone_dialogue_system.ipynb

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Day-by-Day Outline

Day 1: Foundations and Generative AI

Module 1: Foundations of AI and Machine Learning

• Machine Learning vs rule-based programming
• Supervised and unsupervised learning with examples
• ML model development workflow: preprocessing, features, overfitting, evaluation
• Lab: 01_intro_to_ml_concepts.ipynb

Module 2: Deep Learning Primer

• Fundamental concepts of neural networks
• Optimizers, gradient descent, and backpropagation
• Deep learning frameworks: TensorFlow and PyTorch
• Labs:
  • 01_neural_network_basics.ipynb
  • 02_image_classification_pytorch.ipynb

Module 3: Overview of Generative AI

• Introduction to Generative AI and applications
• Probabilistic sampling and latent space concepts
• Autoencoders and Variational Autoencoders (VAEs)
• Diffusion models for image generation
• Labs:
  • 01_intro_to_generative_ai.ipynb
  • 02_autoencoders.ipynb
  • 03_diffusion_models.ipynb

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Day 2: NLP, LLMs, and Intelligent Agents

Module 4: NLP - Understanding Language as Data

• Tokenization and text preprocessing
• Vectorization and embeddings (Word2vec)
• Labs:
  • 01_intro_to_nlp.ipynb
  • 02_tokenization.ipynb
  • 03_embeddings.ipynb

Module 5: Large Language Models (LLMs)

• Pre-trained models: BERT and GPT
• Working with OpenAI and Ollama APIs
• Hugging Face ecosystem tour
• Building UIs with Gradio
• Multimodal AI: Vision-language models (GPT-4V)
• Labs:
  • 01_openai_ollama.ipynb
  • 02_huggingface_tour.ipynb
  • 03_bert_gpt.ipynb
  • 04_gradio_ui.ipynb
  • 05_multimodal_models.ipynb

Module 6: Prompting Techniques and Agentic AI

• Zero-shot, few-shot, and chain-of-thought prompting
• Function calling and tool use (OpenAI and LangChain)
• ReAct agents for autonomous workflows
• Labs:
  • 01_prompting_techniques.ipynb
  • 02_function_calling.ipynb
  • 03_function_calling_langchain.ipynb
  • 04_react_agent.ipynb

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Day 3: RAG, Fine-Tuning, Optimization & Capstone

Module 7: Retrieval-Augmented Generation (RAG)

• RAG architecture with LangChain and LlamaIndex
• Vector databases (Chroma) and semantic search
• RAG evaluation and observability with MLflow
• Labs:
  • 01_rag_langchain.ipynb
  • 02_rag_llamaindex.ipynb
  • 03_rag_evaluation.ipynb

Module 8: Fine-Tuning Large Language Models

• Transfer learning and fine-tuning strategies
• LoRA and Parameter-Efficient Fine-Tuning (PEFT)
• Sentiment analysis with DistilBERT
• Summarization fine-tuning
• Catastrophic forgetting prevention
• Sampling techniques: Temperature, Top-P, Top-K
• Healthcare LLM fine-tuning
• Labs:
  • 01_transfer_learning.ipynb
  • 02_sentiment_analysis.ipynb
  • 02_fine_tuning_openai.ipynb
  • 03_summarization.ipynb
  • 04_sampling_techniques.ipynb
  • 05_Fine_Tuning_LLM_Healthcare.ipynb

Module 9: Model Optimization and Deployment

• Production challenges: memory, cost, latency
• Knowledge distillation (teacher-student training)
• Model pruning (structured and unstructured)
• Quantization (FP16, INT8, INT4, GPTQ, AWQ)
• Benchmarking optimized models
• Deployment strategies for production
• Labs:
  • 01_intro_to_optimization.ipynb
  • 02_knowledge_distillation.ipynb
  • 03_pruning.ipynb
  • 04_quantization.ipynb
  • 05_benchmarking.ipynb

Module 10: Capstone Project

• Build a complete RAG-based dialogue system
• Integrate multiple techniques learned throughout the course
• Lab: capstone_dialogue_system.ipynb

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Quick Launch

Module	Notebook	Open in Colab
Module 01	01 · Intro to ML Concepts
Module 02	01 · Neural Network Basics
Module 02	02 · Image Classification with PyTorch
Module 03	01 · Intro to Generative AI
Module 03	02 · Autoencoders
Module 03	03 · Diffusion Models
Module 04	01 · Intro to NLP
Module 04	02 · Tokenization
Module 04	03 · Embeddings
Module 05	01 · LLM APIs: OpenAI, Ollama, and Gemini
Module 05	02 · Hugging Face Ecosystem
Module 05	03 · BERT vs GPT
Module 05	04 · Building UIs with Gradio
Module 05	05 · Multimodal Models
Module 06	01 · Prompting Techniques
Module 06	02 · Function Calling
Module 06	03 · Function Calling with LangChain
Module 06	04 · ReAct Agent
Module 07	01 · RAG with LangChain
Module 07	02 · RAG with LlamaIndex
Module 07	03 · RAG Evaluation
Module 08	01 · Transfer Learning
Module 08	02 · Sentiment Analysis
Module 08	02 · Fine-Tuning with OpenAI
Module 08	03 · Summarization
Module 08	04 · Sampling Techniques
Module 08	05 · Fine-Tuning LLM Healthcare
Module 09	01 · Intro to Optimization
Module 09	02 · Knowledge Distillation
Module 09	03 · Pruning
Module 09	04 · Quantization
Module 09	05 · Benchmarking
Module 10	Capstone · Dialogue System

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What You'll Learn

By the end of this course, you will be able to:

✅ Build applications with modern LLMs (GPT-4, Claude, Llama, Mistral)
✅ Generate images with diffusion models (Stable Diffusion)
✅ Create multimodal applications using vision-language models (GPT-4V)
✅ Fine-tune models efficiently using LoRA and PEFT techniques
✅ Implement RAG systems with vector databases (Chroma)
✅ Create AI agents with function calling and ReAct patterns
✅ Optimize models for production (distillation, pruning, quantization)
✅ Deploy GenAI applications with proper evaluation and monitoring
✅ Use industry-standard tools (Hugging Face, LangChain, LlamaIndex, MLflow)

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Prerequisites

• Python Programming: Solid understanding of Python, including data structures, control flow, functions, and libraries like NumPy and Pandas
• Machine Learning Fundamentals: Familiarity with supervised/unsupervised learning, model evaluation, and scikit-learn
• Deep Learning Basics: Understanding of neural networks recommended but not required
• API Experience: Helpful to have worked with REST APIs (not required)

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Course Features

• 32 Hands-on Labs: Practical notebooks covering every major topic
• Production-Focused: Learn deployment and optimization techniques
• Modern Tools: Work with 2026 industry-standard frameworks
• Complete Lifecycle: From model selection to production deployment
• Real-World Projects: Build a complete RAG-based dialogue system

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Archived Content

Previous demos and labs are available in the _archive/ folder for reference.