Module 12: Natural Language Processing
Learn to process and understand human language with machine learning.
What You'll Learn
- Text Preprocessing
- Word Embeddings (Word2Vec, GloVe)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Transformers Basics
- Sentiment Analysis
- Text Classification
CNN and RNN curriculum map (RNN & Transformer track)
| Topic | Where to study |
|---|---|
| RNN, LSTM, GRU, bidirectional, tuning | NLP guide |
| PyTorch sequence batches | Sequence data and PyTorch |
| Self-attention, encoder/decoder, inference | Transformers |
| CNNs (images) | Computer vision |
Topics Covered
1. Text Preprocessing
- Tokenization: Split text into words/tokens
- Lowercasing: Normalize case
- Removing Punctuation: Clean text
- Stop Words: Remove common words
- Stemming/Lemmatization: Reduce words to root form
- N-grams: Sequences of n words
2. Word Embeddings
- One-Hot Encoding: Simple but sparse
- Word2Vec: Learn word vectors from context
- Skip-gram and CBOW
- GloVe: Global vectors for word representation
- FastText: Handles out-of-vocabulary words
- Pre-trained Embeddings: Use existing word vectors
3. Recurrent Neural Networks (RNNs)
- Why RNNs: Handle sequences
- Vanilla RNN: Basic recurrent unit
- Limitations: Vanishing gradient problem
- Applications: Language modeling, sequence prediction
4. Long Short-Term Memory (LSTM)
- LSTM Cells: Solve vanishing gradient
- Gates: Forget, Input, Output
- Bidirectional LSTM: Process both directions
- Applications: Sentiment analysis, machine translation
5. Transformers (Basics)
- Attention Mechanism: Focus on relevant parts
- Transformer Architecture: Encoder-Decoder
- BERT: Bidirectional Encoder Representations
- GPT: Generative Pre-trained Transformer
- Hugging Face: Easy access to pre-trained models
6. NLP Tasks
- Sentiment Analysis: Positive/negative classification
- Text Classification: Categorize documents
- Named Entity Recognition (NER): Find entities
- Machine Translation: Translate between languages
- Question Answering: Answer questions from text
Learning Objectives
By the end of this module, you should be able to:
- Preprocess text data
- Create word embeddings
- Build RNN/LSTM models
- Use pre-trained transformer models
- Perform sentiment analysis
- Classify text documents
Projects
- Sentiment Analysis: Classify movie reviews
- Spam Detection: Classify emails (NLP approach)
- Text Classification: Categorize news articles
- Language Model: Generate text
- Chatbot (Basic): Simple conversational agent
Key Concepts
- Sequences: NLP deals with ordered data
- Context: Word meaning depends on context
- Embeddings: Dense vector representations
- Attention: Focus on relevant information
- Transfer Learning: Pre-trained models are powerful
Documentation & Learning Resources
Official Documentation:
- Hugging Face Documentation
- Hugging Face Course: Free comprehensive NLP course
- NLTK Book: Free online book
- spaCy Documentation
- Transformers Documentation
Free Courses:
- CS224n - Stanford NLP: Best NLP course, completely free
- CS224n YouTube Lectures: Full course videos
- NLP Specialization (Coursera): Free audit available
- NLP with Deep Learning (Stanford): Free course
Tutorials:
- NLP Tutorial (Hugging Face)
- Text Classification Tutorial
- Sentiment Analysis Guide
- Transformers Tutorial
- Transformer Fine-Tuning Guide: Fine-tuning T5, BERT, GPT with Hugging Face
- Langchain Guide: Complete Langchain guide for building Gen AI projects
- LlamaIndex Guide: LlamaIndex for data indexing and querying
- AI Agents Guide: Guide to building AI agents with CrewAI, AutoGen, Langgraph, AutoGPT
Video Tutorials:
Practice:
Additional Resources:
- Advanced Topics →: Advanced transformers, sequence-to-sequence, optimization
- Project Tutorial →: Step-by-step sentiment analysis project
- Quick Reference →: Quick lookup guide for NLP
Previous Module: 11-computer-vision
Next Module: 25-generative-ai-llms