English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 50m | 150 MB
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.
Table of Contents
Introduction
1 Gain insights from unstructured text data
2 What you should know
3 Exercise files
Named Entity Recognition (NER)
4 What is named entity recognition (NER)
5 NER with spaCy
6 Data preprocessing for custom NER
7 Custom model training with spaCy
Topic Modeling
8 Introduction to topic modeling
9 Data preprocessing for topic modeling
10 Topic modeling with Gensim
11 Topic modeling visualization with pyLDAvis
12 Model evaluation for topic modeling
Text Summarization
13 What is text summarization
14 Text extraction for summarization
15 Text summarization with sumy
Sentiment Analysis
16 What is sentiment analysis
17 Sentiment analysis with VADER
18 Sentiment analysis with transformers
Conclusion
19 Next steps
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