Course Curriculum

14 Chapters · 14 Lessons

  1. 1

    Book Preview

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  2. 2

    Introduction

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      Introduction

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    Chapter 1 : Introduction to NLP and the Essential Libraries

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      Introduction to NLP and the Essential Libraries

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    Chapter 2 : Building Blocks and Techniques for NLP Algorithms

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      Building Blocks and Techniques for NLP Algorithms

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    Chapter 3 : Sentiment Analysis Using NLP

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      Sentiment Analysis Using NLP

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    Chapter 4 : Deep Learning in NLP

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      Deep Learning in NLP

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    Chapter 5 : Working with CNN

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      Working with CNN

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    Chapter 6 : Building NLP Pipelines Using spaCy

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      Building NLP Pipelines Using spaCy

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    Chapter 7 : Building a spaCy Pipeline for Extracting Information

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      Building a spaCy Pipeline for Extracting Information

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    Chapter 8 : Building a Transformer Using Hugging Face

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      Building a Transformer Using Hugging Face

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    Chapter 9 : Training Language Models

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      Training Language Models

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    Chapter 10 : Importance of Large Language Models and Their Applications

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      Importance of Large Language Models and Their Applications

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    Chapter 11 : Fine-Tuning LLMs and Building Text-Powered Too

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      Fine-Tuning LLMs and Building Text-Powered Too

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    Chapter 12 : Best Practices and Future Trends of NLP

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      Best Practices and Future Trends of NLP

About the Course

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.

Meet Your About

Abhinaba Banerjee holds 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.