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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib A. Basic Concepts B. References Index

Modeling


Data is the lifeline of any scientist, and the selection of data providers is critical in developing or evaluating any statistical inference or machine learning algorithm.

What is a model?

We briefly introduced the concept of a model in the Model categorization section in Chapter 1, Getting Started .

What constitutes a model? Wikipedia provides a reasonably good definition of a model as understood by scientists [2:1]:

A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way.

Models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon or process being represented.

In statistics and probabilistic theory, a model describes data that one might observe from a system to express any form of uncertainty and noise. A model allows us to infer rules, make predictions, and learn from data.

A model is composed of features, also...

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