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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: 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)
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Preface

This book provides an in-depth introduction to natural language processing (NLP) techniques, starting with the mathematical foundations of machine learning (ML) and working up to advanced NLP applications such as large language models (LLMs) and AI applications. As part of your learning experience, you’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing ML and NLP algorithms. You’ll also explore general ML techniques and find out how they relate to NLP. The preprocessing of text data, including methods for cleaning and preparing text for analysis, will follow, right before you learn how to perform text classification, which is the task of assigning a label or category to a piece of text based on its content. The advanced topics of LLMs’ theory, design, and applications will be discussed toward the end of the book, as will the future trends in NLP, which will feature expert opinions on the future of the field. To strengthen your practical skills, you’ll also work on mocked real-world NLP business problems and solutions.

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