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Introduction
1. Access and Preprocess Text
1.1. Access and Analyse Contents of Textfiles
1.2. Access Contents of HTML Page
1.3. Accessing Text with Langchain
1.4. Regular expressions in Python
1.5. Chunking and Tokenisation
2. Word Normalisation
2.1. Morphology
2.2. TextBlob Stemming and Lemmatization
2.3. Correction of Spelling Errors
3. Part-Of-Speech Tagging
3.1. Part-of-speech Tagging
3.2. POS Tagging with NLTK
4. N-Gram Language Model
5. Vector Representations of Words and Documents
5.1. Representations
5.2. Implementation of BoWs
5.3. Implementation of Word-Embeddings
5.4. Contextual Word Embeddings and Text Embeddings
6. Topic Extraction
6.1. Latent Semantic Indexing (LSI)
6.2. Implementation of Topic Extraction and Document Clustering
6.3. Topic Extraction in RSS-Feed Corpus
7. Text Classification
7.1. Validation of Classifiers
7.2. Naive Bayes Text Classification
7.3. Text Classification Application: Fake News detection
7.4. Text Classification Application: Fake News detection
7.5. Text Classification Application: Fake News detection
8. Neural Networks
8.1. Neural Networks Introduction
8.2. Convolutional Neural Networks
8.3. Recurrent Neural Networks
8.4. IMDB Movie Review classification
8.5. Sequence-To-Sequence, Attention, Transformer
8.6. chatGPT
9. LLM Applications
9.1. Langchain Quickstart openAI
9.2. Langchain Quickstart Llama3 with Ollama
10. References
Index
