PLU | Mining Data From Text (2019 EN)

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    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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