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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Machine learning or deep learning

With machine learning, algorithm options are selected and used to analyze data and data sources and, rather than make decisions on them, they learn from them so that they can use patterns or results found in the data to make decisions or predictions about a certain topic, or to solve a specific problem.

What this translates to is that instead of you programming or writing out each rule and instruction that needs to be used for a specific task such as making a prediction, the computer is trained using large amounts of data and algorithms which give it the ability to actually learn how to perform a task, make a prediction, solve a problem, or meet an objective in mind.

Note

Just how much data qualifies as enough data for successful machine learning?

Usually the bigger, the better, but in practice, you must gather a sufficient amount of data, based upon your intended purpose or need. Given a shortage of quantity, the wise data scientist should always focus...

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