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Natural Language Processing: A Textbook & Python Implementation, 2nd Edition

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Study NLP concepts with Python implementation. Learn modern text analytics and machine learning workflows in this textbook.

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Additional information

Additional information

Published On

2025

Language

English

Format

pdf

Size (MB)

27.91 MB

Rating

⭐️⭐️⭐️⭐️⭐️ 4.124

Description

 

Natural Language Processing Python Textbook

Natural Language Processing Python Textbook is your ultimate guide to mastering modern NLP concepts and implementing them using Python. This course offers an in-depth, hands-on learning experience based on the 2nd Edition of the renowned “Natural Language Processing: A Textbook & Python Implementation.” Whether you’re a student, researcher, or developer, this comprehensive course will help you understand how machines interpret, analyze, and generate human language efficiently.


Course Description

In this advanced yet beginner-friendly course, you’ll learn how to bridge the gap between linguistic theory and real-world NLP applications. You’ll explore how Python can be leveraged to process and analyze massive amounts of text data efficiently.
Throughout the lessons, you’ll build NLP models using popular libraries like NLTK, SpaCy, and Transformers, and apply them to practical use cases such as sentiment analysis, text summarization, and chatbot development.

Furthermore, the course delves into deep learning techniques for NLP, covering word embeddings, sequence models, and transformer-based architectures. With easy-to-follow explanations and step-by-step Python implementations, you’ll gain a clear understanding of how modern NLP powers intelligent systems and generative AI.

Explore These Valuable Resources:
NLTK Documentation,
SpaCy Official Site, and
Hugging Face Transformers.


What You’ll Learn

  • Core concepts of Natural Language Processing and Computational Linguistics
  • Preprocessing and cleaning textual data with Python
  • Building NLP pipelines using NLTK and SpaCy
  • Implementing word embeddings and deep learning models for text
  • Understanding BERT and Transformer-based architectures
  • Developing sentiment analysis and text classification projects
  • Implementing chatbots and conversational AI systems

Requirements

  • Basic knowledge of Python programming
  • Familiarity with machine learning fundamentals
  • A computer with internet access and Python 3.x installed

About the Publication

The 2nd Edition of “Natural Language Processing: A Textbook & Python Implementation” is authored by experts in the field of artificial intelligence and computational linguistics. The content integrates academic rigor with real-world coding examples to ensure learners build both theoretical understanding and technical proficiency. Each chapter has been updated with new algorithms, deep learning techniques, and Python examples aligned with today’s NLP frameworks.


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Why Enroll in This Course?

By joining this course, you’ll gain a strong foundation in NLP and Python implementation that directly translates into practical AI development skills. You’ll also be able to apply your knowledge to advanced projects, from text analytics to conversational AI systems. The lessons are structured to help you transition smoothly from fundamental NLP principles to cutting-edge transformer-based models used in industry.

Join now to become proficient in Natural Language Processing and transform your Python skills into AI-driven innovation!


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Additional information

Published On

2025

Language

English

Format

pdf

Size (MB)

27.91 MB

Rating

⭐️⭐️⭐️⭐️⭐️ 4.124

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