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AI Engineering
Python

Natural Language Processing with Python Updated Edition

From Basics to Advanced Projects

From foundational concepts to cutting-edge techniques, and hands-on projects, you'll explore everything you need to master NLP.

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What You'll Get from This Book

13 chapters spanning over 590 pages

More than 820 explanatories blocks of code

More than 50 practical exercises

4 Quizzes to test your knowledge

3 Practical "Real World" Projects

About thIS book

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.

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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.

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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.

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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.

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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.

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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.

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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.

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Additionally, we cover sentiment analysis, topic modeling, and text summarization, giving you the skills to perform comprehensive text analyses and uncover deeper insights.

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Each topic is explained with clarity and precision, supported by practical examples that illustrate their real-world applications.

Natural Language Processing (NLP) is at the heart of modern AI applications, from chatbots and virtual assistants to sentiment analysis and automated translations. With the explosion of text data across industries, mastering NLP is essential for developers and data scientists. Natural Language Processing with Python – Updated Edition provides a practical and hands-on approach to building NLP applications, using the latest advancements in Python and Machine Learning to help you stay competitive in today’s AI-driven world.
This book teaches you how to process, analyze, and extract insights from text data using Python, NLTK, spaCy, and Scikit-Learn. Through step-by-step tutorials, hands-on exercises, and real-world projects, you will develop the ability to: - Clean and preprocess textual data efficiently. - Build and optimize Machine Learning models for NLP tasks. - Work with deep learning techniques for advanced NLP applications. - Apply NLP techniques in practical use cases such as chatbots, sentiment analysis, and text classification. By the end of the book, you'll have the skills to create powerful NLP applications with confidence.
Unlike many NLP books that focus heavily on theory, Natural Language Processing with Python – Updated Edition is built around practical, hands-on learning with: Step-by-step coding examples using modern Python libraries. Real-world projects like chatbot development and text summarization. Feature engineering techniques tailored for NLP tasks. Exercises and quizzes to reinforce your learning. This book ensures you not only understand NLP concepts but also know how to implement them effectively in real-world applications.
Basic Python programming knowledge is recommended, but prior NLP experience is not required. The book starts with foundational concepts and gradually moves to more advanced techniques, making it suitable for both beginners and those looking to deepen their NLP expertise.
Access to the Cuantum Technologies VIP customer service, with a dedicated team of developers ready to answer all your questions. A code repository with fully working examples and pre-tested, production-ready code. The e-learning platform, where you can access additional resources and free video content to reinforce your learning. Regular updates and additional materials to stay updated with new advancements in NLP.

Table of contents

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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

Reviews

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.

Recommended by dozens of people
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Alex M.

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.

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Samantha Morgan

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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