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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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Technical requirements

To effectively read and understand this chapter, it is essential to have a solid foundation in various technical areas. A strong grasp of fundamental concepts in NLP, ML, and linear algebra is crucial. Familiarity with text preprocessing techniques, such as tokenization, stop word removal, and stemming or lemmatization, is necessary to comprehend the data preparation stage.

Additionally, understanding basic ML algorithms, such as logistic regression and support vector machines (SVMs), is crucial for implementing text classification models. Finally, being comfortable with evaluation metrics such as accuracy, precision, recall, and F1 score, along with concepts such as overfitting, underfitting, and hyperparameter tuning, will enable a deeper appreciation of the challenges and best practices in text classification.

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