Hands-On Natural Language Processing
50mAdvanced2022-06-29
Authors
Wuraola Oyewusi
Wuraola Oyewusi is an experienced data scientist, machine learning, and artificial intelligence professional.
Course details
Dexterity at deriving insight from text data is a competitive edge for businesses and individual contributors. This course with instructor Wuraola Oyewusi is designed to help developers make sense of text data and increase their relevance. This is a hands-on course teaching practical application of major natural language processing tasks. Learn how to replicate the knowledge gained into the data that you work with. This course includes a background of each task’s process flow, use cases, and a coding demo. Some of the topics covered are named entity recognition, text summarization, topic modeling, and sentiment analysis.
Skills covered
Natural Language Processing (NLP)Artificial Intelligence (AI)One-Off
Concepts
0. Introduction
- 01 - Gain insights from unstructured text data
- 02 - What you should know
- 03 - Exercise files
1. Named Entity Recognition (NER)
- 04 - What is named entity recognition (NER)
- 05 - NER with spaCy
- 06 - Data preprocessing for custom NER
- 07 - Custom model training with spaCy
2. Topic Modeling
- 08 - Introduction to topic modeling
- 09 - Data preprocessing for topic modeling
- 10 - Topic modeling with Gensim
- 11 - Topic modeling visualization with pyLDAvis
- 12 - Model evaluation for topic modeling
3. Text Summarization
- 13 - What is text summarization
- 14 - Text extraction for summarization
- 15 - Text summarization with sumy
4. Sentiment Analysis
- 16 - What is sentiment analysis
- 17 - Sentiment analysis with VADER
- 18 - Sentiment analysis with transformers
Conclusion
- 19 - Next steps