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  01 - Introduction

   01 - Gain insights from unstructured text data.srt

   02 - What you should know.srt

   03 - Exercise files.srt

  02 - 1. Named Entity Recognition (NER)

   01 - What is named entity recognition (NER).srt

   02 - NER with spaCy.srt

   03 - Data preprocessing for custom NER.srt

   04 - Custom model training with spaCy.srt

  03 - 2. Topic Modeling

   01 - Introduction to topic modeling.srt

   02 - Data preprocessing for topic modeling.srt

   03 - Topic modeling with Gensim.srt

   04 - Topic modeling visualization with pyLDAvis.srt

   05 - Model evaluation for topic modeling.srt

  04 - 3. Text Summarization

   01 - What is text summarization.srt

   02 - Text extraction for summarization.srt

   03 - Text summarization with sumy.srt

  05 - 4. Sentiment Analysis

   01 - What is sentiment analysis.srt

   02 - Sentiment analysis with VADER.srt

   03 - Sentiment analysis with transformers.srt

  06 - Conclusion

   01 - Next steps.srt

  Ex_Files_Hands_On_Natural_Language_Processing

   Exercise Files

    01_02_Named_Entity_Recognition_with_spaCy.ipynb

    01_03_data_annotation_for_named_entity_recognition.csv

    01_04_Custom Named Entity Recognition Model training with spaCy

     base_config.cfg

     default_config.cfg

     train .spacy

    01_04_Custom_Named_Entity_Recognition_Model_training_with_spaCy.ipynb

    02_03_Topic_Modelling_with_Gensim (1).ipynb

    02_05_Model_Evaluation_for_Topic_Modelling.ipynb

    03_03_Abstractive_Summarization_with_Huggingface .ipynb

    04_03_Sentiment_Analysis_with_Transformers.ipynb