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)

Streamlining Text Preprocessing Techniques for Optimal NLP Performance

Text preprocessing stands as a vital initial step in the realm of natural language processing (NLP). It encompasses converting raw, unrefined text data into a format that machine learning algorithms can readily comprehend. To extract meaningful insights from textual data, it is essential to clean, normalize, and transform the data into a more structured form. This chapter provides an overview of the most commonly used text preprocessing techniques, including tokenization, stemming, lemmatization, stop word removal, and part-of-speech (POS) tagging, along with their advantages and limitations.

Effective text preprocessing is essential for various NLP tasks, including sentiment analysis, language translation, and information retrieval. By applying these techniques, raw text data can be transformed into a structured and normalized format that can be easily analyzed using statistical and machine learning methods...