Mastering NLP from Foundations to LLMs: Applying Advanced Techniques from Rule-Based to LLMs for Solving Real World Business Problems

Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends

Key Features

• Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT
• Master embedding techniques and machine learning principles for real-world applications
• Understand the mathematical foundations of NLP and deep learning designs
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python
• Model and classify text using traditional machine learning and deep learning methods
• Understand the theory and design of LLMs and their implementation for various applications in AI
• Explore 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, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.

Table of Contents

1. Navigating the NLP Landscape: A comprehensive introduction
2. Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
3. Unleashing Machine Learning Potentials in NLP
4. Streamlining Text Preprocessing Techniques for Optimal NLP Performance
5. Empowering Text Classification: Leveraging Traditional Machine Learning Techniques
6. Text Classification Reimagined: Delving Deep into Deep Learning Language Models
7. Demystifying Large Language Models: Theory, Design, and Langchain Implementation
8. Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG
9. Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs
10. Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
11. Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

Lior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations.

Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.