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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 3. A Developer's Approach to Data Cleaning 4. Data Mining and the Database Developer 5. Statistical Analysis for the Database Developer 6. Database Progression to Database Regression 7. Regularization for Database Improvement 8. Database Development and Assessment 9. Databases and Neural Networks 10. Boosting your Database 11. Database Classification using Support Vector Machines 12. Database Structures and Machine Learning

Database Structures and Machine Learning

In this final chapter, we will focus on the concepts and types of machine learning.

In keeping with the overall theme of this book, we'll start by offering an explanation of statistical machine learning and related concepts, and then move on to drawing out some similarities between statistical machine learning and basic notions that a reader who has a data or database developer background should be able to relate to.

This chapter is organized into the following areas:

  • Is a data structure a data model?
  • An overview of machine learning concepts
  • The types of machine learning
  • Data developers and machine learning
  • Using R to apply machine learning techniques to a database
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