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Natural Language Processing (NLP) Fundamentals (Video Course), 3rd Edition
- By Bruno Goncalves
- Published Apr 4, 2025 by Addison-Wesley Professional.
Online Video
- Your Price: $239.99
- List Price: $299.99
- About this video
Video accessible from your Account page after purchase.
Description
- Copyright 2025
- Edition: 3rd
- Online Video
- ISBN-10: 0-13-543974-4
- ISBN-13: 978-0-13-543974-6
7 Hours of Video Instruction
Overview
Natural Language Processing LiveLessons covers the fundamentals and some of the more advanced aspects of Natural Language Processing in a simple and intuitive way, empowering you to add NLP to your toolkit. Using the powerful NLTK package, it gradually moves from the basics of text representation, cleaning, topic detection, regular expressions, and sentiment analysis before moving on to the PyTorch deep learning framework to explore advanced topics such as text classification and sequence-to-sequence models. The transformer architectures underlying large language models (LLMs) like ChatGPT, Claude, and BERT are explored in depth along with some practical applications. After successfully completing these lessons youll be equipped with a fundamental and practical understanding of the full breadth of Natural Language Processing tools and algorithms.
Skill Level
Intermediate
Learn How To
* Implement text tokenization and representation
* Use one-hot encodings and bag of words
* Identify relevant words by applying TF-IDF
* Clean text through stemming and lemmatization techniques
* Match patterns using regular expressions
* Understand named entity recognition
* Cluster documents and model topics using various algorithms
* Conduct sentiment analysis, including handling negations and modifiers
* Utilize word embeddings for capturing semantic relationships
* Define GloVe
* Model sequences in PyTorch with RNNs, GRUs, and LSTM networks
* Transfer learning
* Apply language detection
* Understand and apply transformers
* Use LLMs for NLP tasks
* Build practical NLP applications with HuggingFace and large language models
Who Should Take This Course
Data scientists with an interest in natural language processing
Course Requirements
Basic algebra, calculus, and statistics, plus programming experience
Lesson Descriptions
Lesson 1, Text Representations: Unlock the foundations of NLP by mastering the art of transforming raw text into machine-readable formats. Journey from basic tokenization and one-hot encodings to the strategic removal of stop words that clutter your analysis. Discover how TF-IDF reveals the most crucial words in your text, and explore the power of n-grams for capturing meaningful phrases. Cap your learning with a dive into the fascinating world of word embeddings that capture semantic relationships, all reinforced through an engaging hands-on demo.
Lesson 2, Text Cleaning: Take your text processing skills to the next level. Build on your representation knowledge by mastering stemming and lemmatization techniques that strip words to their essential roots and streamline your vocabulary. Wield the precision tool of regular expressions to pinpoint exactly the patterns you need. See these powerful techniques come alive in our practical demonstration that transforms theory into actionable skills.
Lesson 3, Named Entity Recognition: Uncover the hidden structure in your text. Master the crucial skill of part-of-speech tagging to identify the function of each word. Harness chunking and chinking to gather meaningful word groups, then extract the named entities that form the backbone of your content. Watch as raw text transforms before your eyes in our comprehensive end-to-end demonstration of the complete NLP pipeline.
Lesson 4, Topic Modeling: Take a deep dive into the heart of what your texts are actually about. Explore explicit semantic analysis to pinpoint documents discussing specific subjects, then discover how clustering unveils natural topic groupings. Leverage the power of latent semantic analysis and latent-Dirichlet allocation to extract hidden meanings. Unlock the potential of non-negative matrix factorization to identify latent dimensions and drive recommendations. Put it all together in our hands-on workshop that brings these sophisticated techniques within your reach.
Lesson 5, Sentiment Analysis: Move beyond what is said to understand how its said. Decode the emotional undertones of text by distinguishing positive from negative language. Master the subtle art of handling negations and modifiers that can completely flip meaning. Explore corpus-based approaches to precisely measure word valence, with real-world applications demonstrated in our compelling end-of-lesson workshop.
Lesson 6, Text Classification: Harness cutting-edge machine learning for powerful text analysis. See how using PyTorch to implement feed-forward networks and convolutional neural networks can classify movie review sentiment with remarkable accuracy. Explore diverse applications of these techniques across multiple domains, then cement your understanding by building your own classifier in our guided practical session.
Lesson 7, Sequence Modelling: Master the technologies that grasp the flow of language. Build on your classification knowledge with recurrent neural network architectures that capture sequential patterns in text. Progress from basic RNNs to sophisticated gated recurrent units and long short-term memory networks. Explore fascinating auto-encoder models and text generation capabilities before applying everything in a comprehensive demonstration.
Lesson 8, Applications: Connect theory to real-world impact. Explore how the fundamental tools youve mastered drive cutting-edge applications. Discover why word embeddingsparticularly word2vechave revolutionized NLP, allowing complex semantic relationships to be expressed through simple vector algebra. Compare word2vec with its main competitor, GloVe, weighing their respective strengths. Investigate how transfer learning is transforming NLP, and tackle the challenge of language detection, all reinforced with a practical demonstration session.
Lesson 9, NLP with Large Language Models: Step into the frontier of modern NLP. Trace the revolutionary history of large language models and decode the architecture that makes Transformers so powerful. Learn to leverage HuggingFace to rapidly develop state-of-the-art applications for part of speech tagging, named entity recognition, and knowledge extraction. See these cutting-edge techniques in action during our immersive hands-on demonstration that bridges theory and practice.
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Sample Content
Table of Contents
Lesson 1: Text Representation
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
2.1 Stemming
2.2 Lemmatization
2.3 Regular Expressions
2.4 Text Cleaning Demo
Lesson 3: Named Entity Recognition
3.1 Part of Speech Tagging
3.2 Chunking
3.3 Chinking
3.4 Named Entity Recognition
3.5 Demo
Lesson 4: Topic Modeling
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
5.1 Quantify Words and Feelings
5.2 Negations and Modifiers
5.3 Corpus-based Approaches
5.4 Demo
Lesson 6: Text Classification
6.1 Feed Forward Networks
6.2 Convolutional Neural Networks
6.3 Applications
6.4 Demo
Lesson 7: Sequence Modeling
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
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
9.1 Large Language Models
9.2 Transformers
9.3 BERT
9.4 HuggingFace
9.5 NLP Tasks
9.6 Demo