Natural Language Processing with Python Updated Edition
Whether you are a beginner or a seasoned professional, this book will equip you with the skills and knowledge to transform text data into actionable insights. From foundational concepts to cutting-edge techniques, and hands-on projects, you'll explore everything you need to master NLP.
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Unlock the Power of Natural Language Processing
Natural Language Processing (NLP) is at the forefront of technological innovation, transforming the way we interact with data and opening up new possibilities for understanding and generating human language.
With Practical Natural Language Processing with Python: From Basics to Advanced Projects, you will embark on a journey that takes you from the foundational concepts to the cutting-edge techniques used in the field today.
This book is your ultimate guide to mastering NLP with Python, equipping you with the skills needed to tackle real-world challenges and develop sophisticated text processing systems.
Whether you're looking to analyze customer feedback, build intelligent chatbots, or delve into machine translation, this book provides you with the comprehensive knowledge and practical experience you need to succeed.
Master the Fundamentals of NLP
We begin with the essential steps of text preprocessing, where you'll discover techniques to handle raw text data, remove noise, and standardize formats. Tokenization, a critical step, breaks down text into manageable pieces, while stop word removal and lemmatization refine the data further.
Hands-on examples give you a practical understanding of these processes, ensuring you can confidently prepare text data for any NLP task. This foundation is vital, as it sets the stage for more complex analyses and ensures that your data is both accurate and relevant.
Our comprehensive approach ensures you grasp these fundamentals thoroughly, paving the way for your success in more advanced topics.
Delve into the intricacies of language modeling, where you'll learn to create powerful models that can predict and generate text with remarkable accuracy. Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) are covered in detail, providing you with the tools to handle sequential data and long-range dependencies.
Syntax and parsing techniques, such as Parts of Speech (POS) tagging and dependency parsing, allow you to extract meaningful information from text, enhancing your ability to understand and manipulate language structures.
Additionally, we cover sentiment analysis, topic modeling, and text summarization, giving you the skills to perform comprehensive text analyses and uncover deeper insights.
Each topic is explained with clarity and precision, supported by practical examples that illustrate their real-world applications.
Table of contents
Chapter 1: Introduction to NLP
1.1 What is Natural Language Processing (NLP)?
1.2 Significance and Applications of NLP
1.3 Overview of Python for NLP
Practical Exercises
Chapter 1 Summary
Chapter 2: Basic Text Processing
2.1 Understanding Text Data
2.2 Text Cleaning: Stop Word Removal, Stemming, Lemmatization
2.3 Regular Expressions
2.4 Tokenization
Practical Exercises
Chapter 3: Feature Engineering for NLP
3.1 Bag of Words
3.2 TF-IDF
3.3 Word Embeddings (Word2Vec, GloVe)
3.4 Introduction to BERT Embeddings
Practical Exercises
Quiz Part I: Foundations of NLP
Chapter 1: Introduction to NLP
Chapter 2: Basic Text Processing
Chapter 3: Feature Engineering for NLP
Practical Applications
Code Implementation
Chapter 4: Language Modeling
4.1 N-grams
4.2 Hidden Markov Models
4.3 Recurrent Neural Networks (RNNs)
4.4 Long Short-Term Memory Networks (LSTMs)
Practical Exercises
Chapter 5: Syntax and Parsing
5.1 Parts of Speech (POS) Tagging
5.2 Named Entity Recognition (NER)
5.3 Dependency Parsing
Practical Exercises
Chapter Summary
Chapter 6: Sentiment Analysis
6.1 Rule-Based Approaches
6.2 Machine Learning Approaches
6.3 Deep Learning Approaches
Practical Exercises
Chapter Summary
Quiz Part II: Advanced Text Processing and Modeling
Chapter 4: Language Modeling
Chapter 5: Syntax and Parsing
Chapter 6: Sentiment Analysis
Answers
Chapter 7: Topic Modeling
7.1 Latent Semantic Analysis (LSA)
7.2 Latent Dirichlet Allocation (LDA)
7.3 Hierarchical Dirichlet Process (HDP)
Practical Exercises
Chapter Summary
Chapter 8: Text Summarization
8.1 Extractive Summarization
8.2 Abstractive Summarization
Practical Exercises
Chapter Summary
Quiz Part III: Topic Modeling and Text Summarization
Chapter 7: Topic Modeling
Chapter 8: Text Summarization
Answers
Chapter 9: Machine Translation
9.1 Sequence to Sequence Models
9.2 Attention Mechanisms
9.3 Transformer Models
Practical Exercises
Chapter Summary
Chapter 10: Introduction to Chatbots
10.1 What is a Chatbot?
10.2 Applications of Chatbots
10.3 Types of Chatbots: Rule-Based, Self-Learning, and Hybrid
Practical Exercises
Chapter Summary
Chapter 11: Chatbot Project: Personal Assistant Chatbot
11.1 Project Introduction and Design
11.2 Data Collection and Preprocessing
11.3 Building and Training the Chatbot
11.4 Evaluating and Deploying the Chatbot
11.5 Improving and Maintaining the Chatbot
Chapter 12: Project: News Aggregator
12.1 Project Introduction and Design
12.2 Data Collection and Preprocessing
12.3 Implementing Text Summarization and Topic Modeling
12.4 Building the User Interface
12.5 Evaluating and Deploying the Aggregator
Chapter 13: Project: Sentiment Analysis Dashboard
13.1 Project Introduction and Design
13.2 Data Collection and Preprocessing
13.3 Building and Training Sentiment Analysis Models
13.4 Developing the Dashboard Interface
13.5 Evaluating and Deploying the Dashboard
Quiz Part IV: Applications and Advanced Techniques
Chapter 9: Machine Translation
Chapter 10: Introduction to Chatbots
Chapter 11: Chatbot Project: Personal Assistant Chatbot
Chapter 12: Project: News Aggregator
Chapter 13: Project: Sentiment Analysis Dashboard
What our readers are saying about this book
Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.
This book is a treasure trove of information for both beginners and experienced practitioners in the field of Natural Language Processing. The step-by-step approach, combined with practical examples and hands-on projects, makes complex concepts easy to grasp.
I have read many books on NLP, but this one stands out for its comprehensive coverage and practical approach. The projects included in the book are particularly valuable, providing real-world applications that reinforce the theoretical concepts. Whether you're a student, a developer, or a seasoned data professional, this book will elevate your NLP skills to the next level. The authors' expertise and clear explanations make it an essential addition to your library.
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