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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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
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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.
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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