Description
As practitioner of NLP, I am trying to bring many relevant topics under one umbrella in following topics. The NLP has been most talked about for last few years and the knowledge has been spread across multiple places. 1. The content (80% hands on and 20% theory) will prepare you to work independently on NLP projects2. Learn - Basic, Intermediate and Advance concepts3. NLTK, regex, Stanford NLP, TextBlob, Cleaning4. Entity resolution5. Text to Features6. Word embedding7. Word2vec and GloVe8. Word Sense Disambiguation9. Speech Recognition10. Similarity between two strings11. Language Translation12. Computational Linguistics13. Classifications using Random Forest, Naive Bayes and XgBoost14. Classifications using DL with Tensorflow (tf. keras)15. Sentiment analysis16. K-means clustering17. Topic modeling18. How to know models are good enough Bias vs Variance
