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)

Common machine learning models

Here, we will explain some of the most common machine learning models, as well as their advantages and disadvantages. Knowing this information will help you pick the best model for the problem and be able to improve the implemented model.

Linear regression

Linear regression is a type of supervised learning algorithm that’s used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the input features and the output. The goal of linear regression is to find the best-fit line that predicts the value of the dependent variable based on the independent variables.

The equation for a simple linear regression with one independent variable (also called a simple linear equation) is as follows:

<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mrow><mi>y</mi><mo>=</mo><mi>m</mi><mi>x</mi><mo>+</mo><mi>b</mi></mrow></mrow></math>

Here, we have the following:

  • y is the dependent variable (the variable we want to predict)
  • x is the independent variable (the input variable)
  • m is the slope...