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Principles of Data Science

You're reading from   Principles of Data Science Understand, analyze, and predict data using Machine Learning concepts and tools

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789804546
Length 424 pages
Edition 2nd Edition
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Authors (3):
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Sunil Kakade Sunil Kakade
Author Profile Icon Sunil Kakade
Sunil Kakade
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Marco Tibaldeschi Marco Tibaldeschi
Author Profile Icon Marco Tibaldeschi
Marco Tibaldeschi
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Table of Contents (17) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 2. Types of Data 3. The Five Steps of Data Science 4. Basic Mathematics 5. Impossible or Improbable - A Gentle Introduction to Probability 6. Advanced Probability 7. Basic Statistics 8. Advanced Statistics 9. Communicating Data 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials 11. Predictions Don't Grow on Trees - or Do They? 12. Beyond the Essentials 13. Case Studies 14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service Other Books You May Enjoy Index

Chapter 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials

Machine learning has become quite the phrase of the decade. 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 technology and how it will change the way we live.

This chapter focuses on machine learning as a practical part of data science. We will cover the following topics in this chapter:

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

Our aim will be to utilize statistics, probability, and algorithmic thinking in order to understand and apply essential machine learning skills to practical industries, such as marketing. Examples will include predicting...

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