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NLP from Basics to Advanced: Mastering Natural Language Processing

Comprehensive course on Natural Language Processing (NLP) — from tokenization platforms, word embeddings, to Transformer architecture, BERT, GPT. Practice text classification, NER, sentiment analysis, machine translation, question answering and build a production-ready NLP pipeline with Python, Hugging Face, and spaCy.

Introducing the Series

NLP from Basic to Advanced is a course that helps you master the entire field of Natural Language Processing — from theoretical foundations to production practice. The course includes both traditional methods and the most modern techniques of 2026.

🎯 After completing the course, you will:

  • Deeply understand how computers "understand" natural language
  • Proficient in Transformer, BERT, GPT and Hugging Face ecosystem
  • Build NLP applications: text classification, NER, QA, summarization
  • Deploy NLP pipeline production-ready
  • Can handle specific Vietnamese NLP problems

Study path

Part 1: NLP Foundation

  • Lesson 1: What is NLP? — Overview of field and pipeline overview
  • Lesson 2: Text Preprocessing — Tokenization, stemming, lemmatization
  • Lesson 3: Tokenization Deep Dive — BPE, WordPiece, SentencePiece

Part 2: Linguistic Representation

  • Lesson 4: BoW, TF-IDF & N-grams — Classical method
  • Lesson 5: Word Embeddings — Word2Vec, GloVe, FastText
  • Lesson 6: Sentence & Document Embeddings — Sentence-BERT, E5

Part 3: Deep Learning for NLP

  • Lesson 7: RNN & LSTM — Sequential string processing
  • Lesson 8: Attention Mechanism — The turning point of NLP
  • Lesson 9: Transformer — "Attention Is All You Need"

Part 4: Pre-trained Language Models

  • Lesson 10: BERT — Bidirectional Encoder and PhoBERT for Vietnamese
  • Lesson 11: GPT & Autoregressive Models — Generative AI
  • Lesson 12: Hugging Face Ecosystem — Modern NLP practice

Part 5: Applied NLP problems

  • Lesson 13: Text Classification & Sentiment Analysis
  • Lesson 14: Named Entity Recognition (NER)
  • Lesson 15: Question Answering
  • Lesson 16: Text Summarization & Machine Translation

Part 6: NLP Production & Trends

  • Lesson 17: NLP for Vietnamese — Challenges & Solutions
  • Lesson 18: NLP Pipeline Production — MLOps for NLP
  • Lesson 19: Modern LLM & NLP — RAG, Agents, Trends 2026
  • Lesson 20: Capstone Project — Building an end-to-end NLP Platform

Prerequisites

  • Basic Python (variables, functions, classes, list comprehension)
  • Math: Basic Linear Algebra (vector, matrix), probability
  • Basic understanding of Machine Learning (supervised/unsupervised)
  • No previous NLP experience required

Tools used

  • Python 3.10+
  • PyTorch / TensorFlow
  • Hugging Face Transformers, Datasets, Tokenizers
  • spaCy, NLTK, Gensim
  • Google Colab (Free GPU)
  • FastAPI for model serving

Part 1: NLP Foundations — Understanding Language Through a Computer Lens

Lesson 1: What is NLP? — Overview of the field of Natural Language Processing90 minLesson 2: Text Preprocessing — Cleaning and Standardizing Text120 minLesson 3: Tokenization Deep Dive — From Word to BPE, WordPiece, SentencePiece120 min

Part 2: Language Representation — From BoW to Word Embeddings

Lesson 4: Bag of Words, TF-IDF & N-grams — Classical Method90 minLesson 5: Word Embeddings — Word2Vec, GloVe, FastText150 minLesson 6: Sentence & Document Embeddings — From Doc2Vec to Sentence-BERT120 min

Part 3: Deep Learning for NLP — RNN, LSTM, to Transformer

Lesson 7: RNN & LSTM — Sequential Sequence Processing150 minLesson 8: Attention Mechanism — The turning point of NLP150 minLesson 9: Transformer — "Attention Is All You Need"180 min

Part 4: Pre-trained Language Models — BERT, GPT & Beyond

Lesson 10: BERT — Bidirectional Encoder Representations from Transformers180 minLesson 11: GPT & Autoregressive Models — Generative Pre-trained Transformer150 minLesson 12: Hugging Face Ecosystem — Modern NLP practice150 min

Part 5: Applied NLP problems — Hands-on Projects

Lesson 13: Text Classification & Sentiment Analysis180 minLesson 14: Named Entity Recognition (NER) — Entity Extraction150 minLesson 15: Question Answering — Smart Question and Answer System180 minLesson 16: Text Summarization & Machine Translation150 min

Part 6: NLP Production & Modern Trends

Lesson 17: NLP for Vietnamese — Challenges & Solutions150 minLesson 18: NLP Pipeline Production — MLOps for NLP150 minLesson 19: Modern LLM & NLP — RAGs, Agents, and 2026 Trends120 minLesson 20: Capstone Project — Building an end-to-end NLP Platform240 min