Ultimate Natural Language Processing with spaCy and Hugging Face

Key Features
? Learn NLP from scratch with exposure to Deep Learning concepts.
? Build NLP-based projects using the latest frameworks and libraries.
? Define AI-based use cases from scratch, and build NLP applications.

Book Description
Natural Language Processing (NLP) is at the core of modern AI, powering everything from chatbots to recommendation systems.“Ultimate Natural Language Processing with spaCy and Hugging Face”is a practical guide that takes you from essential NLP foundations to advanced transformer models and large language applications, equipping you to build real-world AI projects with confidence.

You begin with the fundamentals—tokenization, lemmatization, Bag-of-Words, TF-IDF, embeddings, POS tagging, and Named Entity Recognition—and apply them to practical use cases such as sentiment analysis, topic classification, and text classification.

The book then moves into Deep Learning for NLP with hands-on coding of CNNs, RNNs, and LSTMs, progressing from theory to applied projects. spaCy is explored in depth, with guidance on building and customizing pipelines for NER, POS tagging, and sentiment analysis. Real-world projects, including extracting dates and events from news articles, ensure that every concept connects to practical applications.

The journey concludes with Hugging Face and transformers, where you train and fine-tune models for summarization, classification, and recommendation. Large Language Models (LLMs) such as GPT, Llama, and Claude are introduced alongside efficient training techniques like LoRA and Retrieval-Augmented Generation. By the end, you will gain the confidence to design and deploy responsible AI-powered solutions.

What you will learn
? Understand NLP fundamentals, including embeddings, POS tagging and NER.
? Implement CNN, RNN and LSTM models for text applications.
? Create and customize spaCy pipelines for real-world NLP tasks.
? Train and fine-tune transformer models using Hugging Face tools.
? Apply large language models to build AI-powered applications.
? Discover responsible AI, RAG and upcoming NLP practices.

Table of Contents
1. Introduction to NLP and the Essential Libraries
2. Building Blocks and Techniques for NLP Algorithms
3. Sentiment Analysis Using NLP
4. Deep Learning in NLP
5. Working with CNN
6. Building NLP Pipelines Using spaCy
7. Building a spaCy Pipeline for Extracting Information
8. Building a Transformer Using Hugging Face
9. Training Language Models
10. Importance of Large Language Models and Their Applications
11. Fine-Tuning LLMs and Building Text-Powered Tools
12. Best Practices and Future Trends of NLP
Index

About the Authors
Abhinaba Banerjeeholds a Master of Science in Big Data Analytics for Business from IESEG School of Management, Lille, France, and a Bachelor of Technology and Master of Technology in Electronics and Communication Engineering from MAKAUT (formerly West Bengal University of Technology), Kolkata, India. He has experience working with Fintech and social media startups in France and is currently employed as a Data Analyst with the Government of Andhra Pradesh. In this role, he works with real-world government data, focusing on data extraction, cleaning, and insight generation.