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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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Data exploration

When working in a methodological environment, datasets are often well known and preprocessed, such as Kaggle datasets. However, in real-world business environments, one important task is to define the dataset from all possible sources of data, explore the gathered data to find the best method for preprocessing it, and ultimately decide on the ML and natural language models that fit the problem and the underlying data best. This process requires careful consideration and analysis of the data, as well as a thorough understanding of the business problem at hand.

In NLP, the data can be quite complex, as it often includes text and speech data that can be unstructured and difficult to analyze. This complexity makes preprocessing an essential step in preparing the data for ML models. The first step of any NLP or ML solution starts with exploring the data to learn more about it, which helps us decide on our path to tackle the problem.

Once the data has been preprocessed...

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