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Principles of Data Science

You're reading from   Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785887918
Length 388 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (20) Chapters Close

Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Index

What is machine learning?


It wouldn't make sense to continue without a concrete definition of what machine learning is. Well, let's back up for a minute. In Chapter 1, How to Sound Like a Data Scientist, we defined machine learning as giving computers the ability to learn from data without being given explicit rules by a programmer. This definition still holds true. Machine learning is concerned with the ability to ascertain certain patterns (signals) out of data, even if the data has inherent errors in it (noise).

Machine learning models are able to learn from data without the explicit help of a human. That is the main difference between machine learning models and classical algorithms. Classical algorithms are told how to find the best answer in a complex system and the algorithm then searches for these best solutions and often works faster and more efficiently than a human. However, the bottleneck here is that the human has to first come up with the best solution. In machine learning,...

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