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Mastering NLP from Foundations to LLMs

You're reading from   Mastering NLP from Foundations to LLMs Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
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Authors (2):
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Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
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Lior Gazit
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP FREE CHAPTER 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

Common machine learning models

Here, we will explain some of the most common machine learning models, as well as their advantages and disadvantages. Knowing this information will help you pick the best model for the problem and be able to improve the implemented model.

Linear regression

Linear regression is a type of supervised learning algorithm that’s used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the input features and the output. The goal of linear regression is to find the best-fit line that predicts the value of the dependent variable based on the independent variables.

The equation for a simple linear regression with one independent variable (also called a simple linear equation) is as follows:

<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mrow><mi>y</mi><mo>=</mo><mi>m</mi><mi>x</mi><mo>+</mo><mi>b</mi></mrow></mrow></math>

Here, we have the following:

  • y is the dependent variable (the variable we want to predict)
  • x is the independent variable (the input variable)
  • m is the slope...
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