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)

Unleashing Machine Learning Potentials in Natural Language Processing

In this chapter, we will delve into the fundamentals of Machine Learning (ML) and preprocessing techniques that are essential for natural language processing (NLP) tasks. ML is a powerful tool for building models that can learn from data, and NLP is one of the most exciting and challenging applications of ML.

By the end of this chapter, you will have gained a comprehensive understanding of data exploration, preprocessing, and data split, know how to deal with imbalanced data techniques, and learned about some of the common ML models required for successful ML, particularly in the context of NLP.

The following topics will be covered in this chapter:

  • Data exploration
  • Common ML models
  • Model underfitting and overfitting
  • Splitting data
  • Hyperparameter tuning
  • Ensemble models
  • Handling imbalanced data
  • Dealing with correlated data