Generative Ai & Llms Foundations: From Basics To Application

Master the core concepts, tools, and applications of Generative AI and Large Language Models (LLMs) in just 8 weeks

What you'll learn

Understand Generative AI & LLMs – Gain a solid foundation of how Generative AI and Large Language Models work, including their core concepts, architectures,

Apply AI in Real-World Scenarios – Learn how to use AI for content generation, code assistance, automation, and industry-specific case studies.

Hands-On Practical Skills – Build and experiment with models through weekly labs, covering tools, frameworks, and fine-tuning techniques.

Evaluate Ethics & Future Opportunities – Explore AI ethics, governance, and responsible innovation, while identifying emerging career and business opportunities

Requirements

Basic Computer Literacy – Comfort with using computers, browsing the internet, and installing software/tools

Familiarity with Python (optional but helpful) – While not mandatory, a beginner-level understanding of Python will help in hands-on labs.

Interest in AI & Technology – Curiosity and willingness to learn are more important than prior experience.

No Advanced Math Needed – The course is designed for beginners; only high-school level math/logical thinking is sufficient.

Description

"This course contains the use of artificial intelligence in creating scripts, visuals, audio, and supporting content"This 8-week course is a complete foundation in Generative AI and Large Language Models (LLMs), designed to help you build both conceptual understanding and practical skills. The program is structured to gradually move from the basics of generative models to advanced applications, customization, safety, and a capstone project that showcases your abilities. The course begins with an introduction to Generative AI, where you will explore tokenization, attention mechanisms, and the transformer architecture that forms the backbone of modern LLMs. You will learn how text generation works, experiment with prompt design, and analyze the impact of model parameters like temperature and top-p on creativity and accuracy. Building on this, the course dives into the foundations of large language models, exploring embeddings, perplexity, and context windows. You will also study core generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models, gaining an intuitive understanding of how these models generate text, images, and structured data. The practical modules allow you to apply Generative AI in practice, including summarization, creative writing, code generation, data augmentation, and image synthesis. You will use modern tools and frameworks like Hugging Face Transformers, LangChain, vector databases (FAISS, Pinecone), and deployment frameworks such as FastAPI and Hugging Face Spaces. You will also learn fine-tuning techniques, including prompt engineering, LoRA (Low-Rank Adaptation), and domain-specific customization, so you can adapt LLMs to specialized tasks. In addition, a dedicated module on ethics, safety, and governance helps you understand and mitigate bias, hallucinations, and responsible AI risks. The course concludes with a capstone project, where you will design, implement, and present a real-world Generative AI application, integrating the skills and frameworks covered throughout the program. By the end, you will be equipped with hands-on experience, a portfolio-ready project, and the confidence to apply Generative AI and LLMs in business, research, and innovation.

Overview

Section 1: Week 1 – Introduction to Generative AI

Lecture 1 1.1: What is Generative AI?

Lecture 2 1.2 Evolution of AI: From Rule-Based to Generative

Lecture 3 1.3 Types of Generative Models (GANs, VAEs, Diffusion, LLMs)

Lecture 4 1.4 Key Applications & Real-World Examples

Lecture 5 Week 1 — Introduction to Generative AI (Hands-On)

Section 2: Week 2 – Foundations of Large Language Models

Lecture 6 2.1 What are LLMs?

Lecture 7 2.2: Transformer Architecture Basics

Lecture 8 2.3: Training Data & Tokenization.

Lecture 9 2.4: Pretraining vs Fine-Tuning

Lecture 10 Week 2 — Foundations of Large Language Models (Hands-On)

Section 3: Week 3 – Core Concepts in Generative AI

Lecture 11 3.1: Natural Language Processing (NLP) Fundamentals

Lecture 12 3.2: Embeddings and Vector Representations

Lecture 13 3.3: Prompt Engineering Basics

Lecture 14 3.4: Evaluation Metrics for Generative AI

Lecture 15 Week 3 — Core Concepts in Generative AI (Hands-On)

Section 4: Week 4 – Generative AI in Practice

Lecture 16 4.1: Hands-On with OpenAI, Hugging Face, or Similar APIs

Lecture 17 4.2: Text Generation, Summarization, and Translation

Lecture 18 4.3: Image & Audio Generation Overview

Lecture 19 4.4: Building Simple Applications with LLMs

Lecture 20 Week 4 — Generative AI in Practice (Hands-On)

Section 5: Week 5 – Fine-Tuning & Customization

Lecture 21 5.1 Transfer Learning for LLMs

Lecture 22 5.2: Fine-Tuning on Domain-Specific Data

Lecture 23 5.3: Retrieval-Augmented Generation (RAG)

Lecture 24 5.4: Low-Rank Adaptation (LoRA) & Parameter-Efficient Fine-Tuning

Lecture 25 Week 5 — Fine-Tuning & Customization (Hands-On)

Section 6: Week 6 – Tools & Frameworks

Lecture 26 6.1 Hugging Face Transformers Library

Lecture 27 6.2: Vector Databases (Pinecone, FAISS, Weaviate)

Lecture 28 Week 6 — Tools & Frameworks (Hands-On)

Section 7: Week 7 – Ethics, Safety & Governance

Lecture 29 7.1 Ethical Concerns in Generative AI

Lecture 30 7.2 Safety & Responsible AI Development.

Lecture 31 7.3 Data Privacy & Security Issues.

Lecture 32 7.4 Future Trends in AI Regulation

Lecture 33 Week 7 — Ethics, Safety & Governance (Hands-On)

Section 8: Week 8 – Applications & Capstone

Lecture 34 8.1 Case Studies: Healthcare, Finance, Education, Creative Industries

Lecture 35 8.2 AI & Human Collaboration in the Future.

Lecture 36 8.3 The Road Ahead for Generative AI.

Lecture 37 8.4 Course Wrap-Up & Key Takeaways

Lecture 38 Week 8 — Applications & Capstone (Hands-On)

Students & Beginners – Anyone curious about AI and looking to build foundational knowledge of Generative AI and LLMs.,Professionals & Career Changers – People in tech, business, or other fields who want to upskill and apply AI in their work.,Entrepreneurs & Innovators – Individuals interested in exploring how Generative AI can create new business opportunities.,Lifelong Learners – Anyone who wants to stay updated with one of the most transformative technologies shaping the future.