The Sneak Peek program provides early access to Pearson video products and is exclusively available to subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. Introduction Natural Language Processing (NLP) Fundamentals: Introduction Lesson 1: Text Representation Topics 1.1 One-hot Encoding 1.2 Bag of Words 1.3 Stop Words 1.4 TF-IDF 1.5 N-grams 1.6 Word Embeddings 1.7 Demo Lesson 2: Text Cleaning Topics 2.1 Stemming 2.2 Lemmatization 2.3 Regular Expressions 2.4 Text Cleaning Demo Lesson 3: Named Entity Recognition Topics 3.1 Part of Speech Tagging 3.2 Chunking 3.3 Chinking 3.4 Named Entity Recognition 3.5 Demo Lesson 4: Topic Modeling Topics 4.1 Explicit Semantic Analysis 4.2 Document Clustering 4.3 Latent Semantic Analysis 4.4 LDA 4.5 Non-negative Matrix Factorization 4.6 Demo Lesson 5: Sentiment Analysis Topics 5.1 Quantify Words and Feelings 5.2 Negations and Modifiers 5.3 Corpus-based Approaches 5.4 Demo Lesson 6: Text Classification Topics 6.1 Feed Forward Networks 6.2 Convolutional Neural Networks 6.3 Applications 6.4 Demo Lesson 7: Sequence Modeling Topics 7.1 Recurrent Neural Networks 7.2 Gated Recurrent Unit 7.3 Long Short-term Memory 7.4 Auto-encoder Models 7.5 Demo Lesson 8: Applications Topics 8.1 Word2vec Embeddings 8.2 GloVe 8.3 Transfer Learning 8.4 Language Detection 8.5 Demo Lesson 9: NLP with Large Language Models Topics 9.1 Large Language Models 9.2 Transformers 9.3 BERT 9.4 HuggingFace 9.5 NLP Tasks 9.6 Demo Summary Natural Language Processing (NLP) Fundamentals: Summary