KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #ai-agents #developer-tools #codex #claude-code #mcp #rust #deepseek-harness #typescript #ai-agent #cli

Mastering-NLP-from-Foundations-to-LLMs

★ 129 Open GitHub ↗

Mastering NLP from Foundations to LLMs, Published by Packt

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Mastering NLP from Foundations to LLMs
```
All codes are refactored/updated per April 2025.
```

<a href="https://www.packtpub.com/product/mastering-nlp-from-foundations-to-llms/9781804619186"><img src="https://m.media-amazon.com/images/I/61ChVewbtSL._SL1233_.jpg" alt="Mastering NLP from Foundations to LLMs" height="256px" align="right"></a>
This is the code repository for [Mastering NLP from Foundations to LLMs](https://www.packtpub.com/product/mastering-nlp-from-foundations-to-llms/9781804619186), published by Packt.

**Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python**

## About Authors:  
 - [Lior Gazit](https://www.linkedin.com/in/liorgazit) is a highly skilled ML professional with a proven track record of success in building and leading teams that use ML to drive business growth. He is an expert in NLP and has successfully developed innovative ML pipelines and products. He holds a master’s degree and has published in peer-reviewed journals and conferences. As a senior director of a ML group in the financial sector and a principal ML advisor at an emerging start-up, 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 ML to drive positive change and growth in his organizations.
  
 - [Meysam Ghaffari](https://www.linkedin.com/in/meysam-ghaffari-ph-d-a2553088/)  is a senior data scientist with a strong background in NLP and deep learning. He currently works at MSKCC, where he specializes in developing and improving ML and NLP models for healthcare problems. He has over nine years of experience in ML and over four 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 the Isfahan University of Technology, and his BS in computer science from Iran University of Science and Technology. He also worked as a post-doctoral research associate at the University of Wisconsin-Madison before joining MSKCC.

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

If you feel this book is for you, get your [copy](https://www.amazon.com/Mastering-NLP-Foundations-LLMs-Techniques/dp/1804619183/ref=sr_1_1?sr=8-1) today!

### 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

## Instructions and Navigations
All of the code is organized into folders.

The code will look like the following:
```
import pandas as pd
import matplotlib.pyplot as plt
# Load the record dict from URL
import requests
import pickle
```

### 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

### Software and Hardware List

| Chapter  | Software required                                                                    | OS required                        |
| -------- | -------------------------------------------------------------------------------------| -----------------------------------|
|  	1-11	   |   	     Access to a Python environment via one of the following: Accessing Google Colab, which is free and easy from any browser on any device (recommended). A local/cloud development environment of Python with the ability to install public packages and access OpenAI’s API | Windows, macOS or Linux |
| 1-11 | Sufficient computation resources, as follows: The previously recommended free access to Google Colab includes a free GPU instance. If opting to avoid Google Colab, the local/cloud environment should have a GPU for several code examples | |

### Table of Contents
1. Navigating the NLP Landscape: A comprehensive introduction
1. Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
1. Unleashing Machine Learning Potentials in NLP
1. Streamlining Text Preprocessing Techniques for Optimal NLP Performance ([Notebooks for chapter 4](https://github.com/PacktPublishing/Mastering-NLP-from-Foundations-to-LLMs/tree/main/Chapter4_notebooks))  
1. Empowering Text Classification: Leveraging Traditional Machine Learning Techniques ([Notebooks for chapter 5](https://github.com/PacktPublishing/Mastering-NLP-from-Foundations-to-LLMs/tree/main/Chapter5_notebooks))  
1. Text Classification Reimagined: Delving Deep into Deep Learning Language Models ([Notebooks for chapter 6](https://github.com/PacktPublishing/Mastering-NLP-from-Foundations-to-LLMs/tree/main/Chapter6_notebooks))  
1. Demystifying Large Language Models: Theory, Design, and Langchain Implementation
1. Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG ([Notebooks for chapter 8](https://github.com/PacktPublishing/Mastering-NLP-from-Foundations-to-LLMs/tree/main/Chapter8_notebooks))  
1. Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs ([Notebooks for chapter 9](https://github.com/PacktPublishing/Mastering-NLP-from-Foundations-to-LLMs/tree/main/Chapter9_notebooks))  
1. Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
1. Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

Mastering-NLP-from-Foundations-to-LLMs

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README