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NLP Tutorial

Learn Natural Language Processing from regular expressions to modern RAG systems, step by step, in simple words. Every idea is shown with animations, real code and real model output.

📚 106 lessons▶ 3:10:07+ of video💻 139 code examples🖼 116 screenshots
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The complete NLP course, free on YouTubeWatch on YouTube ↗

What you will learn

  • Regular expressions, tokenising and cleaning text
  • Bag of words, TF-IDF, topics and text classification
  • Embeddings, Word2Vec, attention and transformers
  • BERT, fine-tuning and large language models
  • RAG: chunking, vector search, reranking and citations
  • Spell checking, search engines, BM25 and evaluation
  • Prompt testing, guardrails, costs and fairness
  • A capstone assistant, quizzes and interview preparation

Course contents

Every lesson has its own page with the explanation, code you can copy, the real output, screenshots from the video, and a link that jumps straight to that moment in the video.

2. Cleaning and Splitting Text

  1. Regular Expressions7:24
  2. Tokenisation9:16
  3. Normalising Text10:46

3. Counting Words

  1. Bag of Words13:01
  2. TF-IDF14:31
  3. Zipf's Law16:22
  4. Topic Modelling17:52

4. Classifying Text

  1. Text Classification19:25
  2. Sentiment Analysis21:37

5. Evaluation and Extraction

  1. Evaluating Text Models23:55
  2. Named Entity Recognition25:37
  3. Parsing Grammar27:53

11. Evaluating and Improving RAG

  1. Evaluating RAG58:06
  2. Advanced RAG59:45

12. Fine-Tuning LLMs

  1. Fine-Tuning LLMs1:01:45
  2. Preparing Training Data1:03:15
  3. LoRA in Practice1:04:31

13. Safety and Tools

  1. Prompt Injection1:06:19
  2. Privacy and Guardrails1:07:47
  3. Giving Scribe Tools1:09:41

14. Languages, Speech, and Scale

  1. Many Languages1:10:53
  2. Speech to Text1:12:36
  3. NLP at Scale1:14:00

15. Language from Counting

  1. N-Gram Language Models1:15:49
  2. Hidden Structure1:18:05

17. Getting the Best from LLMs

  1. Making Models Reason1:25:02
  2. Conversation Memory1:27:03
  3. Parsing Real Documents1:28:34

18. Local Models and Labelling

  1. Running Models Locally1:29:59
  2. Labelling with AI1:31:46

19. Everyday Traps

  1. Which Meaning?1:33:35
  2. Unicode Traps1:35:41
  3. Finding Rare Messages1:38:37

21. Understanding Classifiers

  1. Naive Bayes from Scratch1:47:47
  2. Reading a Classifier1:49:46
  3. Error Analysis1:51:38

22. Classic Algorithms

  1. Tagging with Hidden Markov Models1:53:16
  2. Near Duplicates1:54:57
  3. The Inverted Index1:56:32
  4. BM251:57:25

23. Scoring Language

  1. Autocomplete1:59:01
  2. Scoring Translations2:00:06
  3. Readability2:01:44

24. Practical Modern NLP

  1. Clustering Text2:03:15
  2. Classifying with No Training2:04:45
  3. Bias in Language Models2:05:46

25. Practical Decisions

  1. Text Augmentation2:07:17
  2. Rules Still Win2:08:34
  3. Choosing the Right Tool2:09:26

26. Language Patterns

  1. Finding Phrases2:11:22
  2. Markov Chains2:13:04
  3. Splitting Sentences2:14:22
  4. Handling Negation2:15:36

27. Understanding Customers

  1. Aspect Sentiment2:17:13
  2. Summarising Thousands2:18:42
  3. Spotting Trends2:19:49

28. Building Question Answering

  1. An FAQ Bot2:21:17
  2. Follow-Up Questions2:22:54
  3. Reranking2:23:50
  4. Answers with Citations2:25:17

29. NLP in Production

  1. Testing Prompts2:26:49
  2. Layered Classification2:28:17
  3. Designing Conversations2:29:10
  4. Going Global2:30:41

30. The Big Picture

  1. From ELIZA to Today2:32:04
  2. What LLMs Can't Do2:33:36
  3. Running an NLP Project2:34:55

31. Review: Language Skills

  1. Review: Classic NLP2:36:32
  2. Review: Modern NLP2:40:11
  3. Review: NLP in the Real World2:43:42

32. Myths and Hard Questions

  1. NLP Myths2:47:10
  2. Explaining Language AI2:49:03
  3. Hard NLP Questions2:50:28

34. Quizzes and Interviews

  1. Quiz: Classic NLP2:55:37
  2. Quiz: Modern NLP3:01:10
  3. Interview Questions3:06:32
  4. Your Cheat Sheet3:08:40
  5. Your Roadmap3:10:07