Natural Language Processing, 2nd Edition

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5:23:49

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Lesson 1 Text Representation

1.1 One-hot Encoding.mp4

11:27

1.2 Bag of Words.mp4

01:48

1.3 Stop Words.mp4

02:51

1.4 TF-IDF.mp4

05:46

1.5 N-grams.mp4

03:20

1.6 Working with Word Embeddings.mp4

03:49

1.7 Demo.mp4

27:25

Topics.mp4

00:33

Lesson 2 Text Cleaning

2.1 Stemming.mp4

05:09

2.2 Lemmatization.mp4

04:29

2.3 Regular Expressions.mp4

13:30

2.4 Text Cleaning Demo.mp4

19:29

Topics.mp4

00:24

Lesson 3 Named Entity Recognition

3.1 Part of Speech Tagging.mp4

06:09

3.2 Chunking.mp4

04:14

3.3 Chinking.mp4

02:51

3.4 Named Entity Recognition.mp4

05:40

3.5 Demo.mp4

18:33

Topics.mp4

00:27

Lesson 4 Topic Modeling

4.1 Explicit Semantic Analysis.mp4

05:46

4.2 Document Clustering.mp4

03:23

4.3 Latent Semantic Analysis.mp4

05:36

4.4 LDA Natural.mp4

04:11

4.5 Non-Negative Matrix Factorization.mp4

02:48

4.6 Demo.mp4

27:22

Topics.mp4

00:39

Lesson 5 Sentiment Analysis

5.1 Quantify Words and Feelings.mp4

05:37

5.2 Negations and Modifiers.mp4

03:44

5.3 Corpus-based Approaches.mp4

04:23

5.4 Demo.mp4

14:53

Topics.mp4

00:27

Lesson 6 Text Classification

6.1 Feed Forward Networks.mp4

06:35

6.2 Convolutional Neural Networks.mp4

05:42

6.3 Applications.mp4

04:13

6.4 Demo.mp4

12:05

Topics.mp4

00:26

Lesson 7 Sequence Modeling

7.1 Recurrent Neural Networks.mp4

04:00

7.2 Gated Recurrent Unit.mp4

05:09

7.3 Long Short-term Memory.mp4

05:03

7.4 Auto-encoder Models.mp4

04:16

7.5 Demo.mp4

06:02

Topics.mp4

00:28

Lesson 8 Applications

8.1 Word2vec Embeddings.mp4

11:53

8.2 GloVe.mp4

04:07

8.3 Transfer Learning.mp4

03:08

8.4 Language Detection.mp4

07:51

8.5 Demo.mp4

22:28

Topics.mp4

00:50

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