Mining Data from Text

Focused View

Janani Ravi

2:21:24

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1.Course Overview

01.Course Overview.mp4

01:40

2.Modeling Text Using Natural Language Processing

02.Module Overview.mp4

01:17

03.Prerequisites and Course Outline .mp4

01:03

04.Mining Data from Text.mp4

01:49

05.Numeric Representations of Text - One Hot Encoding.mp4

04:22

06.Numeric Representations of Text - Frequency Based Encodings.mp4

04:56

07.Numeric Representations of Text - Prediction Based Embeddings.mp4

03:15

08.Feature Hashing.mp4

03:18

09.Bag of Words - Bag of N Grams.mp4

02:28

10.Install and Setup.mp4

02:28

11.Frequency Based Representation Using Bag of Words and Bag of N Grams Model.mp4

06:59

12.Representing Documents Using TFIDF Scores and Feature Hashes.mp4

06:01

13.Module Summary.mp4

01:30

3.Building Classification Models Using Text Data

14.Module Overview.mp4

01:12

15.Naive Bayes Classifier.mp4

04:04

16.Sentiment Analysis Using the Naive Bayes Classifier.mp4

06:44

17.scikit-learn Pipelines to Build Features .mp4

04:42

18.Multiclass Classification.mp4

04:37

19.Module Summary.mp4

01:10

4.Understanding Topic Modeling

20.Module Overview.mp4

01:34

21.Topic Modeling.mp4

07:23

22.Topic Modeling Algorithms.mp4

05:40

23.Module Summary.mp4

01:31

5.Implementing Topic Modeling

24.Module Overview.mp4

01:18

25.Latent Dirichlet Allocation - Topic Modeling with the Newspaper Headlines Dataset.mp4

05:52

26.Visualizing Topic Assignments Using Manifold Learning to Reduce Dimensions.mp4

05:24

27.Latent Dirichlet Allocation - Topic Modeling with the DBPedia Dataset.mp4

06:40

28.Visualizing Topics Using Manifold Learning to Reduce Dimensions .mp4

06:48

29.Interactive Topic Model Visualization Using PyLDAVis.mp4

02:42

30.Non-negative Matrix Factorization - Topic Modeling with the DBPedia Dataset.mp4

04:08

31.Interactive Topic Visualization Using Bokeh.mp4

03:37

32.Latent Semantic Indexing - Preprocessing Text.mp4

05:28

33.Concept Modeling Using LSI.mp4

04:34

34.Module Summary.mp4

01:16

6.Understanding and Implementing Keyword Extraction

35.Module Overview.mp4

01:14

36.Understanding RAKE for Keyword Extraction.mp4

04:09

37.Keyword Extraction Using RAKE.mp4

06:58

38.Summary and Further Study.mp4

01:33

Description

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.

What You'll Learn?

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.

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Data Science

Artificial Intelligence

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.