📝 Natural Language Processing (NLP) Introduction Series v1.0

Master practical skills in handling text data, from NLP fundamentals to state-of-the-art technologies including Transformers, BERT, and GPT

Series Overview

This series is a practical educational content comprising 5 chapters that progressively teaches the theory and implementation of Natural Language Processing (NLP) from the ground up.

Natural Language Processing (NLP) is the technology that enables computers to understand and process human language. Starting with foundational techniques such as tokenization and preprocessing, this series covers word vectorization using TF-IDF and Word2Vec, deep learning models like RNN/LSTM and Seq2Seq, Self-Attention mechanisms and Transformer architecture, large-scale pre-trained models such as BERT and GPT, and practical applications including sentiment analysis, named entity recognition, question answering, and summarization. Many services we use daily—such as Google Translate, ChatGPT, voice assistants, and search engines—are powered by NLP technology. Natural language processing has become an essential skill for AI engineers, data scientists, and researchers, and is applied across a wide range of domains including document classification, machine translation, information extraction, and dialogue systems. The series provides practical knowledge using Python libraries such as Hugging Face Transformers, spaCy, and Gensim.

Features:

• ✅ From Theory to Practice: Systematic learning from NLP foundational concepts to cutting-edge technologies
• ✅ Implementation-Focused: Over 50 executable Python/Transformers code examples
• ✅ State-of-the-Art Compliant: Theory and implementation of Transformers, BERT, GPT, and LLMs
• ✅ Practical Applications: Real-world practice in sentiment analysis, NER, QA, and summarization
• ✅ Progressive Learning: Structured progression: Fundamentals → Deep Learning → Transformers → LLMs → Applications

Total Learning Time: 6-7 hours (including code execution and exercises)

How to Study

Recommended Learning Path

For Beginners (No NLP Knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Duration: 6-7 hours

For Intermediate Learners (ML Experience):
- Chapter 1 (Review) → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 4-5 hours

Topic-Specific Enhancement:
- Foundational Techniques (Tokenization, TF-IDF, Word2Vec): Chapter 1 (Focused Study)
- Deep Learning (RNN/LSTM, Seq2Seq, Attention): Chapter 2 (Focused Study)
- Transformers & BERT: Chapter 3 (Focused Study)
- GPT, LLMs & Prompt Engineering: Chapter 4 (Focused Study)
- Practical Applications (Sentiment Analysis, NER, QA, Summarization): Chapter 5 (Focused Study)
- Duration: 70-90 minutes per chapter

Chapter Details

Chapter 1: NLP Fundamentals

Difficulty: Beginner
Reading Time: 70-80 minutes
Code Examples: 12

Learning Content

1. What is NLP - Definition, application areas, challenges
2. Tokenization - Word segmentation, morphological analysis, subword tokenization
3. Preprocessing - Normalization, stopword removal, stemming, lemmatization
4. TF-IDF - Word importance calculation, document vectorization
5. Word2Vec - Word distributed representations, CBOW, Skip-gram

Learning Goals

• ✅ Understand fundamental concepts and application areas of NLP
• ✅ Implement tokenization and preprocessing techniques
• ✅ Vectorize documents using TF-IDF
• ✅ Obtain word distributed representations using Word2Vec
• ✅ Build basic text processing pipelines

Read Chapter 1 →

Chapter 2: Deep Learning and NLP

Difficulty: Beginner to Intermediate
Reading Time: 80-90 minutes
Code Examples: 11

Learning Content

1. RNN (Recurrent Neural Network) - Sequential data processing, vanishing gradient problem
2. LSTM (Long Short-Term Memory) - Learning long-term dependencies, gating mechanisms
3. Seq2Seq (Sequence-to-Sequence) - Encoder-decoder architecture
4. Attention Mechanism - Attention mechanisms, alignment
5. Bidirectional LSTM - Understanding context from both directions

Learning Goals

• ✅ Understand the mechanisms and challenges of RNN/LSTM
• ✅ Implement Seq2Seq models
• ✅ Explain the operational principles of Attention mechanisms
• ✅ Implement sequential data classification and generation tasks
• ✅ Train and evaluate deep learning models

Read Chapter 2 →

Chapter 3: Transformer & BERT

Difficulty: Intermediate
Reading Time: 80-90 minutes
Code Examples: 10

Learning Content

1. Transformer Architecture - Self-Attention, Multi-Head Attention, positional encoding
2. BERT (Bidirectional Encoder Representations from Transformers) - Pre-training, Masked Language Model
3. Fine-tuning - Task adaptation, transfer learning, hyperparameter tuning
4. Hugging Face Transformers - Model loading, tokenizers, inference
5. BERT Variants - RoBERTa, ALBERT, DistilBERT

Learning Goals

• ✅ Understand the Transformer mechanism
• ✅ Explain the computation method of Self-Attention
• ✅ Implement document classification tasks using BERT
• ✅ Become proficient in using Hugging Face Transformers
• ✅ Fine-tune pre-trained models

Read Chapter 3 →

Chapter 4: Large Language Models

Difficulty: Intermediate
Reading Time: 80-90 minutes
Code Examples: 9

Learning Content

1. GPT (Generative Pre-trained Transformer) - Autoregressive language models, generation tasks
2. LLM (Large Language Models) - GPT-3/4, LLaMA, Claude
3. Prompt Engineering - Prompt design, Few-shot Learning, Chain-of-Thought
4. In-Context Learning - In-context learning, Zero-shot/Few-shot inference
5. LLM Evaluation and Limitations - Bias, hallucination, ethical challenges

Learning Goals

• ✅ Understand the differences between GPT and BERT
• ✅ Explain the mechanisms of large language models
• ✅ Design effective prompts
• ✅ Implement Few-shot Learning and Chain-of-Thought
• ✅ Understand the limitations and ethical challenges of LLMs

Read Chapter 4 →

Chapter 5: NLP Applications

Difficulty: Intermediate
Reading Time: 80-90 minutes
Code Examples: 12

Learning Content

1. Sentiment Analysis - Positive/negative classification, sentiment scoring
2. Named Entity Recognition (NER) - Extraction of person names, location names, organization names
3. Question Answering - Extractive QA, generative QA
4. Text Summarization - Extractive summarization, generative summarization
5. Machine Translation - Neural machine translation, evaluation metrics (BLEU)

Learning Goals

• ✅ Implement sentiment analysis systems
• ✅ Train and evaluate named entity recognition models
• ✅ Build question answering systems
• ✅ Implement text summarization models
• ✅ Develop practical NLP applications

Read Chapter 5 →

Overall Learning Outcomes

Upon completing this series, you will acquire the following skills and knowledge:

Knowledge Level (Understanding)

• ✅ Explain NLP fundamental concepts and text processing techniques
• ✅ Understand the mechanisms of RNN/LSTM, Transformers, and BERT
• ✅ Explain the operational principles of Large Language Models (LLMs)
• ✅ Understand the characteristics and evaluation methods of each NLP task
• ✅ Explain the differences between Attention mechanisms and Self-Attention

Practical Skills (Doing)

• ✅ Implement text preprocessing and tokenization
• ✅ Vectorize documents using TF-IDF and Word2Vec
• ✅ Use the Transformers library to utilize models
• ✅ Fine-tune BERT to adapt to specific tasks
• ✅ Implement sentiment analysis, NER, QA, and summarization systems

Application Skills (Applying)

• ✅ Select appropriate NLP models for specific tasks
• ✅ Design effective prompts
• ✅ Train models on custom datasets
• ✅ Evaluate and improve NLP model performance
• ✅ Design and implement practical NLP applications

Prerequisites

To effectively study this series, the following knowledge is desirable:

Required (Must Have)

• ✅ Python Fundamentals: Variables, functions, classes, modules
• ✅ NumPy Basics: Array operations, numerical computation
• ✅ Machine Learning Fundamentals: Training, validation, and testing concepts
• ✅ Linear Algebra Basics: Vectors, matrices, inner products
• ✅ Probability and Statistics Basics: Probability distributions, expected values

Recommended (Nice to Have)

• 💡 Deep Learning Fundamentals: Neural networks, backpropagation
• 💡 PyTorch/TensorFlow: Experience using deep learning frameworks
• 💡 English Literature Comprehension: For understanding technical papers and documentation
• 💡 Git/GitHub: Version control for models and code
• 💡 Regular Expressions: For efficient text processing

Recommended Prerequisite Learning:

• 📚 - ML fundamentals