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 projects

2. Learn - Basic, Intermediate and Advance concepts

3. NLTK, regex, Stanford NLP, TextBlob, Cleaning

4. Entity resolution

5. Text to Features

6. Word embedding

7. Word2vec and GloVe

8. Word Sense Disambiguation

9. Speech Recognition

10. Similarity between two strings

11. Language Translation

12. Computational Linguistics

13. Classifications using Random Forest, Naive Bayes and XgBoost

14. Classifications using DL with Tensorflow (tf.keras)

15. Sentiment analysis

16. K-means clustering

17. Topic modeling

18. How to know models are good enough Bias vs Variance