Enhance your NLP skills foundational math, complete code samples, and expert insights on current and future trends
Key Features
- Get in-depth coverage of the technical aspects of NLP, including proof-based explanations of key concepts and techniques
- Benefit from real-world use cases and code examples of complete system design to better understand how NLP can be applied to solve business problems
- Gain an understanding of the mathematical foundations of NLP and how they can be used to develop effective solutions for a variety of business scenarios
Book Description
This book provides an in-depth introduction to natural language processing (NLP) techniques, starting with the mathematical foundations of machine learning 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 machine learning and NLP algorithms. You'll also explore general machine learning techniques and find out how they relate to NLP. Preprocessing of text data, including methods for cleaning and preparing text for analysis will follow, right before you learn how to do 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 towards 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 sample real world NLP business problems and solutions.
What you will learn
- Master the mathematical foundations of machine learning and NLP, including linear algebra, optimization, probability, and statistics
- Implement techniques for preprocessing text data and preparing it for analysis
- Explore ML-NLP system design in Python and apply it in practice
- Get to grips with methods for modeling and classifying text using traditional machine learning and later using deep learning methods
- Understand the theory and design of LLMs and their implementation for various applications in AI
- Gain NLP insights, trends, and expert opinions on its future direction and potential
Who this book is for
This book is for deep learning and machine learning researchers, hands-on NLP practitioners, ML/NLP educators, and students. Professionals working with text as part of their projects and existing NLP practitioners will also find plenty of useful information in this book. Beginner-level machine learning knowledge and a basic working knowledge of Python will help you get the best out of this book.
