Learn Generative AI and LLMs in 7 Weeks

A comprehensive 7-week program to master Generative AI, LLMs, and AI agents. From foundations to advanced applications, this course provides hands-on experience and practical skills.

Learn Generative AI and LLMs in 7 Weeks

Published 2025-01-07 · By Shahzad Asghar

Learn Generative AI, LLMs, and AI Agents in 7 Weeks

Overview

This 7-week program is designed to provide a comprehensive understanding of Generative AI, Large Language Models (LLMs), and AI Agents. Through a mix of interactive lectures, hands-on exercises, curated resources, and a capstone project, you'll gain the skills needed to build real-world applications and excel in the field of AI.

Week 1: Foundations of AI, Machine Learning, and Generative AI

Goal: Build a strong foundation in AI, ML, and generative models.

Day 1
Introduction to AI
- Video: The History of AI by ColdFusion
- Reading: Artificial Intelligence: A Guide to Intelligent Systems (Chapter 1)
Day 2
Basics of Machine Learning
- Video: Machine Learning Explained by Google Cloud
- Reading: A Few Useful Things to Know About Machine Learning (PDF)
Day 3
Introduction to Deep Learning
- Interactive Tool: TensorFlow Playground
- Reading: Deep Learning by Ian Goodfellow (Chapter 1, Free PDF)
Day 4
Basics of Generative AI
- Video: What is Generative AI? by IBM Technology
- Reading: Generative Deep Learning by David Foster (Chapter 1)
Day 5
Introduction to Large Language Models
- Interactive Demo: OpenAI GPT-3 Playground
- Reading: The Illustrated Transformer by Jay Alammar
Day 6
Recap and Q&A
Task: Reflect on the week's learning.

Week 2: Understanding Language Models and Transformers

Goal: Dive deeper into language models and the Transformer architecture.

Day 1
Introduction to NLP
- Video: NLP Demystified by Hugging Face
- Reading: Speech and Language Processing by Daniel Jurafsky (Chapter 1)
Day 2
Transformer Architecture
- Video: The Transformer Model by Hugging Face
- Paper: Attention is All You Need (arXiv)
Day 3
Pretraining and Fine-Tuning
- Tutorial: Hugging Face Fine-Tuning Course
- Paper: BERT: Pre-training of Deep Bidirectional Transformers (arXiv)
Day 4
Applications of LLMs
- Demo: Hugging Face Spaces
- Paper: Language Models are Few-Shot Learners (GPT-3, arXiv)
Day 5
Limitations and Ethics
- Article: On the Dangers of Stochastic Parrots (ACM)
Day 6
Recap and Q&A
Task: Write a short essay on ethical considerations of LLMs.

Week 3: Building and Fine-Tuning LLMs

Goal: Learn how to build and fine-tune LLMs for specific tasks.

Day 1
Setting Up Your Environment
- Reading: Hugging Face Documentation
Day 2
Fine-Tuning LLMs
- Tutorial: Hugging Face Fine-Tuning Guide
Day 3
Transfer Learning in NLP
- Article: Transfer Learning in NLP by Sebastian Ruder
Day 4
Hyperparameter Tuning
- Article: Practical Guide to Hyperparameter Optimization
Day 5
Evaluating LLMs
- Article: Evaluation Metrics for Language Models
Day 6
Recap and Q&A
Task: Submit a report on your fine-tuning experiment.

Week 4: Advanced Topics in Generative AI

Goal: Explore advanced concepts like GANs, VAEs, and multimodal models.

Day 1
Introduction to GANs
- Video: Generative Adversarial Networks by Two Minute Papers
- Paper: Generative Adversarial Networks by Goodfellow et al. (arXiv)
Day 2
Introduction to VAEs
- Tutorial: Implementing VAEs in PyTorch
Day 3
Multimodal Models
- Demo: DALL-E or CLIP
- Paper: Learning Transferable Visual Models (CLIP, arXiv)
Day 4
RL with Human Feedback
- Article: Reinforcement Learning with Human Feedback (OpenAI Blog)
Day 5
Scaling Laws in LLMs
- Paper: Scaling Laws for Neural Language Models (arXiv)
Day 6
Recap and Q&A
Task: Reflect on advanced generative models.

Week 5: Real-World Applications and Deployment

Goal: Learn to deploy LLMs and build applications.

Day 1
Building Chatbots
- Tutorial: Building Chatbots with Hugging Face
Day 2
Deploying Models
- Tutorial: Deploying Models with FastAPI
Day 3
Optimizing for Production
- Tutorial: Model Quantization with Hugging Face
Day 4
Multimodal Applications
- Tutorial: Building Multimodal Apps with CLIP
Day 5
Monitoring Models
- Article: MLOps: Continuous Delivery for ML
Day 6
Recap and Q&A
Task: Submit a deployment project report.

Week 6: Future Trends and Capstone Project

Goal: Explore emerging trends and complete a capstone project.

Day 1
Future of Generative AI
- Video: The Future of AI by Yann LeCun
Day 2-5
Capstone Project Work
Task: Build a real-world application (e.g., chatbot, summarization tool).
Day 6
Capstone Project Presentation
Task: Submit and present your project.

Week 7: Understanding and Building AI Agents

Goal: Learn about AI agents and their real-world applications.

Day 1
Introduction to AI Agents
- Video: Introduction to AI Agents
Day 2-6
Building and Deploying AI Agents
Task: Build and deploy an AI agent (e.g., personal assistant, game-playing agent).
Day 7
Capstone Project for Agents
Task: Present and demonstrate your AI agent.

Key Takeaways

  • Gain hands-on experience with Generative AI, LLMs, and AI Agents.
  • Build and deploy real-world applications.
  • Understand the ethical implications and future trends in AI.

By the end of this program, you'll be equipped with the knowledge and skills to excel in the rapidly evolving field of AI.

Written by Shahzad Asghar — Head of Data and Digital Solutions at UN-ESCWA, with 20+ years building AI and data systems across UNHCR, UNICEF, and UNOCHA. His team built UNHCR’s first global IVR appointment system, serving 700,000+ refugees. He created the Last-Mile AI Framework. Read more about this UN AI expert

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