From Zero to Mastery: A Complete NLP and LLM Engineering Learning Roadmap

Against the backdrop of large language models (LLMs) sweeping the globe, the open-source learning project maintained by Farizakb provides a systematic learning path from basic mathematics to cutting-edge AI applications. This project covers six major modules, combining theoretical explanations with runnable code examples, building a complete tech stack from traditional NLP to modern generative AI, suitable for developers at different stages.

• Original Author/Maintainer: Farizakb
• Source Platform: GitHub
• Original Title: AI_Engineering-LLM
• Original Link: https://github.com/Farizakb/AI_Engineering-LLM
• Release Date: May 24, 2026

The project breaks down complex AI engineering knowledge into six progressive modules, each containing theoretical explanations and code practices, forming a structured learning journey.

Module 1: Deep Learning and Math Fundamentals

Starting with NumPy vectorization operations, it covers logistic regression, shallow neural network implementation, understanding the essence of backpropagation and gradient descent, laying the foundation for subsequent complex architectures.

Module 2: Basic NLP Techniques

Systematically explains text processing workflows (tokenization, cleaning, standardization), traditional models (Bag of Words, TF-IDF, Word2Vec) and applications (sentiment analysis, NER), comparing the evolution of RNN and LSTM.

Module 3: In-depth Analysis of Word Embeddings

Covers methods like Word2Vec (CBOW/Skip-gram), GloVe, FastText, and visualizes vector spaces via PCA/t-SNE.

Module 4: Core of Transformer Architecture

Analyzes positional encoding, decoding strategies (temperature adjustment, Top-K/Top-P), in-depth attention mechanisms (multi-head self-attention, sparse attention, Flash Attention), MoE models, and end-to-end Q&A implementation.

Module 5: RAG System Practice

Provides 8 cases: text chunking optimization, document index management, and vertical domain Q&A bots such as CRM automation and financial analysis, including Kaggle-level complete RAG construction.

Module 6: AI Agent Development

Based on the LangChain framework, it explains core Agent technologies: prompt chains, PDF RAG, tool integration, conversation memory, advanced Agent design with multi-tool combinations, and introduces LCEL declarative programming.

Each module is equipped with complete, directly runnable and modifiable code examples; the RAG module includes vertical domain practice cases; the Agent module implements advanced agents with multi-tool integration, directly corresponding to actual industrial needs.

The unique value of the project lies in its systematicness and practicality: it provides a progressive path for beginners, fills gaps for experienced developers, offers talent training materials for team leaders, and connects technologies to solve practical problems.

It is recommended to go deep step by step according to the module order, and selectively study specific modules based on one's own project needs; the best way to master AI engineering technology is to practice hands-on on the basis of understanding the principles.