Book Content
chapters • 10h12m total length
1. What is Text Analysis?
2. Python Tips for Text Analysis
3. spaCy’s Language Models
4. Gensim – Vectorizing text and transformations and n-grams
5. POS-Tagging and its Applications
6. NER-Tagging and its Applications
7. Dependency Parsing
8. Top Models
9. Advanced Topic Modelling
10. Clustering and Classifying Text
11. Similarity Queries and Summarization
12. Word2Vec, Doc2Vec and Gensim
13. Deep Learning for text
14. Keras and spaCy for Deep Learning
15. Sentiment Analysis and ChatBots
