PLU | Mining Data From Text (2019 EN)

Company: Pluralsight
Author: Janani Ravi
Full Title: Mining Data From Text
Year: 2019
Language: English
Genre: Educational: Big Data
Skill Level: Intermediate
Price: -
-
Files: MP4 (+ Code Files, Slides .PDF)
Time: 02:08:36
Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 450 kbps
Audio: AAC at 70 Kbps, 2 channels, 44.1 KHz



This course discusses text and document feature vectors that can be passed into machine learning models, topic modeling using Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, and keyword extraction using RAKE.

A large part of the appeal of deep learning models is their ability to work with unstructured data types such as text, images, and video. However such models are only as good as the feature vectors that they operate on. In this course, Mining Data from Text, you will gain the ability to build highly optimized and efficient feature vectors from textual and document data. First, you will learn how to represent documents as numeric data using simple numeric identifiers for individual words as well as more elegant methods such as term frequency and inverse document frequency. Next, you will discover how to perform topic modeling using techniques such as latent semantic analysis, latent Dirichlet allocation, and non-negative matrix factorization. Finally, you will explore how to implement keyword extraction using a popular algorithm - RAKE. When you’re finished with this course, you will have the skills and knowledge to move on to build efficient and optimized feature vectors from a large document corpus and use those feature vectors in building powerful machine learning models.


Lessons:
1. Course Overview
01. Course Overview
2. Modeling Text Using Natural Language Processing
02. Module Overview
03. Prerequisites and Course Outline
04. Mining Data from Text
05. Numeric Representations of Text: One Hot Encoding
06. Numeric Representations of Text: Frequency Based Encodings
07. Numeric Representations of Text: Prediction Based Embeddings
08. Feature Hashing
09. Bag of Words: Bag of N Grams
10. Install and Setup
11. Frequency Based Representation Using Bag of Words and Bag of N Grams Model
12. Representing Documents Using TFIDF Scores and Feature Hashes
13. Module Summary
3. Building Classification Models Using Text Data
14. Module Overview
15. Naive Bayes Classifier
16. Sentiment Analysis Using the Naive Bayes Classifier
17. scikit-learn Pipelines to Build Features
18. Multiclass Classification
19. Module Summary
4. Understanding Topic Modeling
20. Module Overview
21. Topic Modeling
22. Topic Modeling Algorithms
23. Module Summary
5. Implementing Topic Modeling
24. Module Overview
25. Latent Dirichlet Allocation: Topic Modeling with the Newspaper Headlines Dataset
26. Visualizing Topic Assignments Using Manifold Learning to Reduce Dimensions
27. Latent Dirichlet Allocation: Topic Modeling with the DBPedia Dataset
28. Visualizing Topics Using Manifold Learning to Reduce Dimensions
29. Interactive Topic Model Visualization Using PyLDAVis
30. Non-negative Matrix Factorization: Topic Modeling with the DBPedia Dataset
31. Interactive Topic Visualization Using Bokeh
32. Latent Semantic Indexing: Preprocessing Text
33. Concept Modeling Using LSI
34. Module Summary
6. Understanding and Implementing Keyword Extraction
35. Module Overview
36. Understanding RAKE for Keyword Extraction
37. Keyword Extraction Using RAKE
38. Summary and Further Study

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