Download Python Natural Language Processing Cookbook, 2E by Zhenya Antić (.PDF)

Python Natural Language Processing Cookbook: Over 60 recipes for building powerful NLP solutions using Python and LLM libraries, 2nd Edition by Zhenya Antić and Saurabh Chakravarty
Requirements: .PDF reader, 7 mb
Overview: Harness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.

You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK.

You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust and transparency in your NLP models.

By the end of this book, you’ll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Genre: Non-Fiction > Tech & Devices

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• Understand fundamental NLP concepts along with their applications using examples in Python
• Classify text quickly and accurately with rule-based and supervised methods
• Train NER models and perform sentiment analysis to identify entities and emotions in text
• Explore topic modeling and text visualization to reveal themes and relationships within text
• Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks
• Use question-answering techniques to handle both open and closed domains
• Apply XAI techniques to better understand your model predictions

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