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)

Summary

In this chapter, we embarked on a comprehensive exploration of text classification, an indispensable aspect of NLP and ML. We delved into various types of text classification tasks, each presenting unique challenges and opportunities. This foundational understanding sets the stage for effectively tackling a broad range of applications, from sentiment analysis to spam detection.

We walked through the role of N-grams in capturing local context and word sequences within text, thereby enhancing the feature set used for classification tasks. We also illuminated the power of the TF-IDF method, the role of Word2Vec in text classification, and popular architectures such as CBOW and skip-gram, giving you a deep understanding of their mechanics.

Then, we introduced topic modeling and examined how popular algorithms such as LDA can be applied to text classification.

Lastly, we introduced a professional paradigm for leading an NLP-ML project in a business or research setting....