Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)

Technical requirements

To follow along with the examples and exercises in this chapter on text preprocessing, you will need a working knowledge of a programming language such as Python, as well as some familiarity with NLP concepts. You will also need to have certain libraries installed, such as Natural Language Toolkit (NLTK), spaCy, and scikit-learn. These libraries provide powerful tools for text preprocessing and feature extraction. It is recommended that you have access to a Jupyter Notebook environment or another interactive coding environment to facilitate experimentation and exploration. Additionally, having a sample dataset to work with can help you understand the various techniques and their effects on text data.

Text normalization is the process of transforming text into a standard form to ensure consistency and reduce variations. Different techniques are used for normalizing text, including lowercasing, removing special characters, spell checking, and stemming or lemmatization...