Natural Language Processing (NLP) Fundamentals, 3rd Edition
ISBN: 9780135439692 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 6h 14m | 1.34 GB
Instructor: Bruno Goncalves
ISBN: 9780135439692 | .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 6h 14m | 1.34 GB
Instructor: Bruno Goncalves
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Introduction
Natural Language Processing (NLP) Fundamentals: Introduction
Lesson 1: Text Representation
Topics
1.1 One-hot Encoding
1.2 Bag of Words
1.3 Stop Words
1.4 TF-IDF
1.5 N-grams
1.6 Word Embeddings
1.7 Demo
Lesson 2: Text Cleaning
Topics
2.1 Stemming
2.2 Lemmatization
2.3 Regular Expressions
2.4 Text Cleaning Demo
Lesson 3: Named Entity Recognition
Topics
3.1 Part of Speech Tagging
3.2 Chunking
3.3 Chinking
3.4 Named Entity Recognition
3.5 Demo
Lesson 4: Topic Modeling
Topics
4.1 Explicit Semantic Analysis
4.2 Document Clustering
4.3 Latent Semantic Analysis
4.4 LDA
4.5 Non-negative Matrix Factorization
4.6 Demo
Lesson 5: Sentiment Analysis
Topics
5.1 Quantify Words and Feelings
5.2 Negations and Modifiers
5.3 Corpus-based Approaches
5.4 Demo
Lesson 6: Text Classification
Topics
6.1 Feed Forward Networks
6.2 Convolutional Neural Networks
6.3 Applications
6.4 Demo
Lesson 7: Sequence Modeling
Topics
7.1 Recurrent Neural Networks
7.2 Gated Recurrent Unit
7.3 Long Short-term Memory
7.4 Auto-encoder Models
7.5 Demo
Lesson 8: Applications
Topics
8.1 Word2vec Embeddings
8.2 GloVe
8.3 Transfer Learning
8.4 Language Detection
8.5 Demo
Lesson 9: NLP with Large Language Models
Topics
9.1 Large Language Models
9.2 Transformers
9.3 BERT
9.4 HuggingFace
9.5 NLP Tasks
9.6 Demo
Summary
Natural Language Processing (NLP) Fundamentals: Summary
Natural Language Processing (NLP) Fundamentals: Introduction
Lesson 1: Text Representation
Topics
1.1 One-hot Encoding
1.2 Bag of Words
1.3 Stop Words
1.4 TF-IDF
1.5 N-grams
1.6 Word Embeddings
1.7 Demo
Lesson 2: Text Cleaning
Topics
2.1 Stemming
2.2 Lemmatization
2.3 Regular Expressions
2.4 Text Cleaning Demo
Lesson 3: Named Entity Recognition
Topics
3.1 Part of Speech Tagging
3.2 Chunking
3.3 Chinking
3.4 Named Entity Recognition
3.5 Demo
Lesson 4: Topic Modeling
Topics
4.1 Explicit Semantic Analysis
4.2 Document Clustering
4.3 Latent Semantic Analysis
4.4 LDA
4.5 Non-negative Matrix Factorization
4.6 Demo
Lesson 5: Sentiment Analysis
Topics
5.1 Quantify Words and Feelings
5.2 Negations and Modifiers
5.3 Corpus-based Approaches
5.4 Demo
Lesson 6: Text Classification
Topics
6.1 Feed Forward Networks
6.2 Convolutional Neural Networks
6.3 Applications
6.4 Demo
Lesson 7: Sequence Modeling
Topics
7.1 Recurrent Neural Networks
7.2 Gated Recurrent Unit
7.3 Long Short-term Memory
7.4 Auto-encoder Models
7.5 Demo
Lesson 8: Applications
Topics
8.1 Word2vec Embeddings
8.2 GloVe
8.3 Transfer Learning
8.4 Language Detection
8.5 Demo
Lesson 9: NLP with Large Language Models
Topics
9.1 Large Language Models
9.2 Transformers
9.3 BERT
9.4 HuggingFace
9.5 NLP Tasks
9.6 Demo
Summary
Natural Language Processing (NLP) Fundamentals: Summary