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)

Explaining the preprocessing pipeline

We will explain a complete preprocessing pipeline that has been provided by the authors to you, the reader.

As shown in the following code, the input is a formatted text with encoded tags, similar to what we can extract from HTML web pages:

"<SUBJECT LINE> Employees details<END><BODY TEXT>Attached are 2 files,\n1st one is pairoll, 2nd is healtcare!<END>"

Let’s take a look at the effect of applying each step to the text:

  1. Decode/remove encoding:

    Employees details. Attached are 2 files, 1st one is pairoll, 2nd is healtcare!

  2. Lowercasing:

    employees details. attached are 2 files, 1st one is pairoll, 2nd is healtcare!

  3. Digits to words:

    employees details. attached are two files, first one is pairoll, second is healtcare!

  4. Remove punctuation and other special characters:

    employees details attached are two files first one is pairoll second is healtcare

  5. Spelling corrections:

    employees details...