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)

Types of text classification

Text classification is an NLP task where ML algorithms assign predefined categories or labels to text based on its content. It involves training a model on a labeled dataset to enable it to accurately predict the category of unseen or new text inputs. Text classification methods can be categorized into three main types – supervised learning, unsupervised learning, and semi-supervised learning:

  • Supervised learning: This type of text classification involves training a model on labeled data, where each data point is associated with a target label or category. The model then uses this labeled data to learn the patterns and relationships between the input text and the target labels. Examples of supervised learning algorithms for text classification include naive bayes, SVMs, and neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
  • Unsupervised learning: This type of text classification involves...