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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

From statistics to machine learning

In this section, we step beyond the known confines of statistics. We’re about to investigate a field that’s become the beating heart of business intelligence and innovation – machine learning.

What is machine learning?

Machine learning is a subfield of AI that employs statistical techniques to enable computer systems to learn from data. It centers on developing algorithms that can learn patterns from data to make predictions or decisions. The keyword here is learn, as unlike rule-based algorithms within computer science, machine learning systems operate by training a model based on input data, then using that model to make predictions or understand patterns in the data, rather than following static program instructions.

To put it simply, think of a child first learning to speak. After hearing many people speak around them – parents, relatives, and friends – the child learns language and grammar without...

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