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Data Science for Decision Makers

You're reading from   Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781837637294
Length 270 pages
Edition 1st Edition
Languages
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Author (1):
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Jon Howells Jon Howells
Author Profile Icon Jon Howells
Jon Howells
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding Data Science and Its Foundations
2. Chapter 1: Introducing Data Science FREE CHAPTER 3. Chapter 2: Characterizing and Collecting Data 4. Chapter 3: Exploratory Data Analysis 5. Chapter 4: The Significance of Significance 6. Chapter 5: Understanding Regression 7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls
8. Chapter 6: Introducing Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Interpreting and Evaluating Machine Learning Models 12. Chapter 10: Common Pitfalls in Machine Learning 13. Part 3: Leading Successful Data Science Projects and Teams
14. Chapter 11: The Structure of a Data Science Project 15. Chapter 12: The Data Science Team 16. Chapter 13: Managing the Data Science Team 17. Chapter 14: Continuing Your Journey as a Data Science Leader 18. Index 19. Other Books You May Enjoy

Supervised Machine Learning

Within machine learning, supervised learning is one of the most used and most useful subfields. It is often the first area students learn within machine learning and what people think of when first hearing about machine learning, as it involves learning on annotated or labeled data, similar to how we learn from correct examples.

The applications of supervised machine learning are wide and varied. From the spam detection on your email inbox, through to recommendation systems used when recommending TV shows and movies on your favorite streaming service, through to the call you get from your bank when its systems believe they may have detected fraudulent transactions, these are all applications of supervised machine learning.

In this chapter, we will discuss in more detail the steps involved in training and deploying supervised machine learning models, some of the core supervised machine learning models, factors to consider when training and evaluating...

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