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Natural Language Processing with Python

Make computers understand you! This book explores Natural Language Processing (NLP) with Python. Turn text into data & build applications like chatbots & sentiment analysis.

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About thIS book

Unlock the Power of Language: Master Natural Language Processing with Python

This book equips you with the essential skills and knowledge to master NLP using Python, one of the most popular languages for machine learning tasks. Through engaging explanations, practical exercises, and real-world applications, you'll delve into the fascinating world of NLP, exploring:

  • Text Processing and Cleaning: Learn how to prepare text data for analysis, removing noise and inconsistencies to ensure accurate results.
  • Sentiment Analysis: Discover how to uncover the emotions and opinions expressed within text data, valuable for customer reviews, social media analysis, and market research.
  • Topic Modeling: Uncover the hidden thematic structures within large text corpora, enabling the classification of documents and understanding of complex topics.
  • Chatbots and Virtual Assistants: Explore the concepts behind building intelligent conversational interfaces that can respond to user queries in a natural and engaging way.

Make Machines Understand Language: A Practical Guide to NLP with Python

This book dives right into the hands-on application of NLP techniques using Python. Through a series of engaging projects and real-world scenarios, you'll gain practical experience in areas like:

  • Building a sentiment analysis model: Learn to identify positive, negative, and neutral sentiment within text data, unlocking valuable insights from customer reviews, social media posts, and more.
  • Creating a text summarization tool: Discover how to automatically generate concise summaries of lengthy documents, saving you time and effort while extracting key information.
  • Developing a chatbot: Explore the concepts behind building intelligent conversational agents that can understand and respond to user queries in a natural and engaging way.

Imagine a world where computers can understand and respond to human language just like you and me. This power lies within the realm of Natural Language Processing (NLP), and "Natural Language Processing with Python" is your gateway to unlocking its potential.

In our ever-evolving world, the ability to communicate effectively with machines is becoming increasingly crucial. "Natural Language Processing with Python" empowers you to bridge the gap between humans and computers by providing a practical guide to NLP.

Whether you're a data scientist seeking to expand your skillset, a programmer looking to explore new frontiers, or simply interested in the fascinating world of language and AI, "Natural Language Processing with Python" provides the perfect starting point. By the end of this journey, you'll be well-equipped to not only understand NLP concepts but also apply them to solve real-world problems and unlock the power of language for yourself and others.

This book is designed for both aspiring and experienced programmers and data scientists. With its clear instructions, real-world case studies, and readily available Python libraries, you'll gain the necessary skills and knowledge to make machines understand language and unlock a world of possibilities in various domains.

Ready to embark on your journey into the exciting world of NLP? Get your copy of "Natural Language Processing with Python" today!

Table of contents

Chapter 1: Introduction to NLP

1.1 What is Natural Language Processing (NLP)?

1.2 Importance and Applications of NLP

1.3 Overview of Python for NLP

Chapter 2: Setting Up the Environment

2.1 Setting Up an Integrated Development Environment (IDE)

2.2 Python Basics: A Refresher

2.3 Working with Virtual Environments in Python

2.4 Introduction to Jupyter Notebooks

2.5 Installing Necessary Libraries

Chapter 3: Basic Text Processing

3.1 Understanding Text Data

3.2 Text Cleaning: Stop Word Removal, Stemming, Lemmatization

3.3 Regular Expressions

3.4: Tokenization

3.5. Practical Exercises of Chapter 3: Basic Text Processing

Chapter 4: Feature Engineering for NLP

4.1 Bag of Words

4.2 TF-IDF

4.3 Word Embeddings

4.4 Introduction to BERT Embeddings

Chapter 4 Conclusion of Feature Engineering for NLP

Chapter 5: Language Modeling

5.1 N-grams

5.2: Hidden Markov Models

5.3 Recurrent Neural Networks (RNNs)

5.4 Long Short-Term Memory Networks (LSTMs)

5.5 Practical Exercises of Chapter 5: Language Modeling

Chapter 6: Syntax and Parsing

6.1 Parts of Speech (POS) Tagging

6.2 Named Entity Recognition (NER)

6.3 Dependency Parsing

6.4 Constituency Parsing

6.5 Semantic Role Labeling

Chapter 7: Sentiment Analysis

7.1 Rule-Based Approaches

7.2 Machine Learning Approaches

7.3 Deep Learning Approaches

7.4 Sentiment Analysis Applications and Challenges

7.5 Practical Exercises of Chapter 7: Sentiment Analysis

Chapter 8: Topic Modelling

8.1 Latent Semantic Analysis (LSA)

8.2 Latent Dirichlet Allocation (LDA)

8.3 Hierarchical Dirichlet Process (HDP)

8.4 Non-negative Matrix Factorization (NMF)

8.5 Practical Exercises of Chapter 8: Topic Modelling

Chapter 9: Text Summarization

9.1 Extractive Summarization

9.2 Abstractive Summarization

9.3 Applications of Text Summarization

9.4 Practical Exercises of Chapter 9: Text Summarization

Chapter 9 Conclusion of Text Summarization

Chapter 10: Machine Translation

10.1 Sequence to Sequence Models

10.2 Attention Mechanisms

10.3 Transformer Models

10.4 Neural Machine Translation Evaluation Metrics

10.5 Practical Exercises of Chapter 10: Machine Translation

Chapter 11: Introduction to Chatbots

11.1 What is a Chatbot?

11.2 Applications of Chatbots

11.3 Types of Chatbots: Rule-Based, Self-Learning, and Hybrid

11.4 Designing a Chatbot: Principles and Best Practices

11.5 Understanding Natural Language Processing in Chatbots

Chapter 12: Chatbot Project: Customer Support Chatbot

12.1 Project Introduction and Design

12.2 Data Collection and Preprocessing

12.3 Building and Training the Chatbot

12.4 Improving and Maintaining the Chatbot

12.5 Complete Project Code

Chapter 13: Advanced Topics

13.1 Transfer Learning in NLP

13.2 Natural Language Understanding (NLU)

13.3 Natural Language Generation (NLG)

13.4 Advanced Transformer Models (GPT, BERT, RoBERTa, etc.)

13.5 Practical Exercises of Chapter 13: Advanced Topics

Chapter 14: Ethics in NLP

14.1 Bias in NLP

14.2 Privacy Concerns

14.3 Misinformation and Fake News

Chapter 14 Conclusion of Ethics in NLP

Chapter 15: Future Trends in NLP

15.1 Improved Language Models

15.2 Low-Resource Languages

15.3 Multimodal NLP

15.4 Explainable NLP

15.5 Ethical Considerations


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
Review from Amazon


I am not actually a programmer, but this book seems well laid-out and easy to understand for the technologically-inclined. It is well-written with step by step instructions and should be helpful for Python programmers and people interested in NLP.

Review from Amazon


This is one of the very best primers I have ever come across: extremely well-organized, well-written, and comprehensive. This should become the authoritative book on the subject. If you are interested in learning about Natural Language Processing with Python you need look no further!

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