From the eBook - 1st edition.
Chapter 1: Introduction to NLP
Introduction to natural language processing
Gensim and its NLP modeling techniques
Topic modeling with BERTopic
Common NLP Python modules included in this book
Chapter 2: Text Representation
Shining applications of BoW and TF-IDF
scikit-learn for BoW (CountVectorizer)
Chapter 3: Text Wrangling and Preprocessing
Key steps in NLP preprocessing
NLTK for stop-word removal
Gensim for stop-word removal
Building a pipeline with spaCy
Part 2: Latent Semantic Analysis/Latent Semantic Indexing
Chapter 4: Latent Semantic Analysis with scikit-learn
Understanding matrix operations
The determinant of a matrix
Understanding a transformation matrix
A transformation matrix in daily life examples
Understanding eigenvectors and eigenvalues
Coding truncatedSVD with scikit-learn
Using TruncatedSVD for LSI with real data
Using TruncatedSVD to build a model
Chapter 5: Cosine Similarity
What is cosine similarity?
How cosine similarity is used in images
How to compute cosine similarity with scikit-learn
Chapter 6: Latent Semantic Indexing with Gensim
Performing text preprocessing
Performing word embedding with BoW and TF-IDF
Using the coherence score to find the optimal number of topics
Saving the model for production
Using the model as an information retrieval tool
Loading the dictionary list
Preprocessing the new document
Scoring the document to get the latent topic scores
