NLP & LLMs from Scratch: A Comprehensive Guide with Python

Welcome to "NLP & LLMs from Scratch"! This course is designed to be a comprehensive, step-by-step journey into the fascinating worlds of Natural Language Processing (NLP) and Large Language Models (LLMs). Starting from the foundational concepts, we will progressively build your understanding and skills, enabling you to process, analyze, and generate human language using Python.

Course Goal: To equip you with the theoretical knowledge and practical Python skills to understand core NLP techniques, grasp the architecture and functionality of LLMs like BERT and GPT, and apply these to build real-world language-powered applications.

What You'll Learn:

• Foundations: Core principles of NLP, Python essentials for text processing, and an introduction to key libraries like NLTK and spaCy.
• Core NLP Techniques: Text preprocessing (normalization, stemming, lemmatization), Part-of-Speech tagging, Named Entity Recognition.
• Representing Text: Traditional methods like Bag-of-Words and TF-IDF, and modern approaches using Word Embeddings (Word2Vec, GloVe, FastText).
• Deep Learning for NLP: Recurrent Neural Networks (RNNs, LSTMs, GRUs) and their application to sequence data.
• The Transformer Era: In-depth understanding of the Attention mechanism and the Transformer architecture.
• Large Language Models (LLMs): Concepts behind models like BERT and GPT, including pre-training and fine-tuning.
• Working with LLMs: Prompt engineering, parameter-efficient fine-tuning (PEFT) like LoRA, and Retrieval Augmented Generation (RAG).
• Advanced Topics: Evaluating LLMs, deployment considerations, and crucial ethical implications.
• Practical Application: Hands-on projects to solidify your learning and build portfolio-worthy pieces.

Target Audience:

• Aspiring NLP engineers and data scientists looking for a structured learning path.
• Software developers aiming to integrate NLP/LLM capabilities into their applications.
• Students and researchers wanting to understand the fundamentals of modern language technology.
• Anyone curious about how computers make sense of and generate human language.

Prerequisites:

1. Basic Python Programming:
   • Familiarity with variables, data types (strings, lists, dictionaries).
   • Understanding of control flow (if/else statements, for/while loops).
   • Ability to write and use functions.
   • Basic knowledge of classes and objects is helpful but not strictly required for early modules.
2. Basic Mathematical Concepts (Conceptual Understanding):
   • Linear Algebra: Vectors and matrices (we'll review as needed).
   • Probability: Basic concepts of probability and distributions.
   • These will be explained intuitively as they arise, but prior exposure is beneficial.
3. Setup:
   • A working Python 3 environment (Python 3.8+ recommended). We suggest using Anaconda or venv for managing packages.
   • A code editor (e.g., VS Code, PyCharm, Jupyter Notebook/Lab).
   • Git for version control (to follow along with the repository).

Course Structure:

The course is divided into modules, each focusing on a specific area of NLP/LLMs. Each module contains several sessions with detailed explanations, concepts, and Python code examples/exercises.

• Module 1: Foundations of NLP and Python for Text
• Module 2: Core NLP Techniques
• Module 3: Feature Engineering and Traditional Machine Learning for NLP
• Module 4: Word Embeddings
• Module 5: Deep Learning for NLP - RNNs and LSTMs/GRUs
• Module 6: The Transformer Architecture and Attention Mechanism
• Module 7: Introduction to Large Language Models (LLMs) - BERT, GPT
• Module 8: Working with LLMs - Prompt Engineering and Fine-tuning Basics
• Module 9: Advanced LLM Concepts and Applications
• Module 10: Building NLP/LLM Projects and Ethical Considerations
• Capstone Project

License:

This course material is licensed under the MIT License. You are free to use, modify, and distribute this content, but please provide attribution.

How to Use This Repository:

1. Clone the repository: git clone <repository-url>
2. Navigate to the Course Directory: Each module has its own folder (e.g., Module_01_Foundations/).
3. Follow Session READMEs: Each session within a module (e.g., Session_1.1_Intro_NLP/) has a README.md file. This file contains the core learning material, explanations of concepts, and references to associated Python code.
4. Run the Code: Python scripts (.py) and Jupyter Notebooks (.ipynb) are provided for hands-on practice.
5. Engage and Experiment: Modify the code, try new examples, and explore the concepts further.

Let's begin our journey into the exciting field of NLP and LLMs!