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 covered a range of techniques and methods for text preprocessing, including normalization, tokenization, stop word removal, POS tagging, and more. We explored different approaches to these techniques, such as rule-based methods, statistical methods, and deep learning-based methods. We also discussed the advantages and disadvantages of each method and provided examples and code snippets to illustrate their use.

At this point, you should have a solid understanding of the importance of text preprocessing and the various techniques and methods available for cleaning and preparing text data for analysis. You should be able to implement these techniques using popular libraries and frameworks in Python and understand the trade-offs between different approaches. Furthermore, you should have a better understanding of how to process text data to achieve better results in NLP tasks such as sentiment analysis, topic modeling, and text classification.

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