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Principles of Data Science

You're reading from   Principles of Data Science A beginner's guide to essential math and coding skills for data fluency and machine learning

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837636303
Length 326 pages
Edition 3rd Edition
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Toc

Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Data Science Terminology 2. Chapter 2: Types of Data FREE CHAPTER 3. Chapter 3: The Five Steps of Data Science 4. Chapter 4: Basic Mathematics 5. Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability 6. Chapter 6: Advanced Probability 7. Chapter 7: What Are the Chances? An Introduction to Statistics 8. Chapter 8: Advanced Statistics 9. Chapter 9: Communicating Data 10. Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials 11. Chapter 11: Predictions Don’t Grow on Trees, or Do They? 12. Chapter 12: Introduction to Transfer Learning and Pre-Trained Models 13. Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift 14. Chapter 14: AI Governance 15. Chapter 15: Navigating Real-World Data Science Case Studies in Action 16. Index 17. Other Books You May Enjoy

How to Tell if Your Toaster is Learning – Machine Learning Essentials

It seems as though every time we hear about the next great start-up or turn on the news, we hear something about a revolutionary piece of machine learning (ML) or artificial intelligence (AI) technology and how it will change the way we live. This chapter focuses on ML as a practical part of data science. We will cover the following topics in this chapter:

  • Defining different types of ML, along with examples of each kind
  • Regression and classification
  • What is ML, and how is it used in data science?
  • The differences between ML and statistical modeling and how ML is a broad category of the latter
  • An Introduction to Linear Regression

Our aim in this chapter will be to utilize statistics, probability, and algorithmic thinking in order to understand and apply essential ML skills to practical industries, such as marketing. Examples will include predicting star ratings of restaurant reviews...

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