🟦 Practical Large Language Models (LLMs) Hands-On Course

This course provides a practical, hands-on introduction to Large Language Models (LLMs), focusing on how modern AI systems understand, process, and generate human language. It is structured in a progressive way so that learners build knowledge step by step, starting from basic concepts and moving toward more advanced real-world applications such as semantic search and Retrieval-Augmented Generation (RAG).

Learners will gain both conceptual understanding and practical insight into how LLMs work internally, how text is represented numerically, and how these models are applied to solve real-world NLP problems such as classification, clustering, and text generation. The course also emphasizes prompt engineering and modern AI application patterns used in production systems.

By the end of the course, learners will understand the core mechanisms behind LLMs and how to apply them effectively in data analysis, automation, and intelligent AI-driven applications.

🟨 1. Introduction to Large Language Models

This section introduces the basic concept of LLMs and their role in modern artificial intelligence systems.

🟩 1.1 What Are Large Language Models?

Understand what LLMs are and how they generate human-like text based on learned patterns from data.

🟩 1.2 High-Level Model Functioning

Learn how LLMs process input and produce outputs using deep learning architectures.

🟩 1.3 Importance of LLMs in AI

Explore why LLMs are a core technology in modern AI applications such as chatbots and assistants.

🟨 2. Tokens and Embeddings

This section explains how text is transformed into numerical representations for machine learning models.

🟩 2.1 What Are Tokens?

Learn how text is broken into smaller units called tokens for processing.

🟩 2.2 Tokenization Process

Understand how words and subwords are converted into token IDs.

🟩 2.3 Embeddings Representation

Explore how tokens are transformed into dense vector representations that capture meaning.

🟨 3. Inside Large Language Models

This section provides insight into the internal workings of LLM architectures.

🟩 3.1 Transformer-Based Architecture

Understand how transformer models process sequential data.

🟩 3.2 Attention Mechanism

Learn how models focus on relevant parts of input text.

🟩 3.3 Internal Model Processing

Explore how information flows inside neural networks during inference.

🟨 4. Text Classification with LLMs

This section shows how LLMs can categorize and organize textual data.

🟩 4.1 What is Text Classification?

Learn how AI assigns labels to text based on content.

🟩 4.2 Practical Classification Use Cases

Explore spam detection, sentiment analysis, and document labeling.

🟩 4.3 LLM-Based Classification Techniques

Understand how modern LLMs improve classification accuracy.

🟨 5. Text Clustering and Topic Modeling

This section focuses on discovering patterns and grouping similar texts.

🟩 5.1 What is Text Clustering?

Learn how similar documents are grouped together automatically.

🟩 5.2 Topic Modeling Concepts

Understand how AI identifies hidden themes in large datasets.

🟩 5.3 Applications in Real Data Analysis

Explore use cases in research, marketing, and content analysis.

🟨 6. Prompt Engineering Fundamentals

This section teaches how to control LLM outputs using well-designed prompts.

🟩 6.1 Writing Effective Prompts

Learn how to structure instructions for better AI responses.

🟩 6.2 Improving Output Quality

Understand how prompt clarity affects model performance.

🟩 6.3 Prompt Optimization Techniques

Explore iterative refinement strategies for better results.

🟨 7. Text Generation Techniques

This section explains how LLMs generate coherent and context-aware text.

🟩 7.1 How Text Generation Works

Learn how models predict the next token to generate text.

🟩 7.2 Controlling Output Structure

Understand how to guide format, tone, and style of generated content.

🟩 7.3 Applications of Text Generation

Explore writing assistants, summarization tools, and chat systems.

🟨 8. Semantic Search & RAG Systems

This section introduces advanced retrieval-based AI systems.

🟩 8.1 What is Semantic Search?

Learn how AI retrieves meaning-based results instead of keyword matching.

🟩 8.2 Introduction to RAG

Understand Retrieval-Augmented Generation and how it enhances LLMs.

🟩 8.3 Combining LLMs with External Knowledge

Explore how external data sources improve AI accuracy and reliability.

🟨 9. Final Learning Outcomes

By the end of this course, learners will understand how Large Language Models work internally, how text is represented using tokens and embeddings, and how advanced NLP tasks such as classification, clustering, generation, and retrieval are implemented. They will also be able to apply prompt engineering and build practical AI applications using LLMs and RAG systems in real-world scenarios.