Book description
Leverage Scala and Machine Learning to construct and study systems that can learn from data
In Detail
The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from selfdriving cars, engineering designs, biometrics, and trading strategies, to detection of genetic anomalies.
The book begins with an introduction to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.
Next, you'll learn about data preprocessing and filtering techniques. Following this, you'll move on to clustering and dimension reduction, Naïve Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and reenforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.
What You Will Learn
 Build dynamic workflows for scientific computing
 Leverage open source libraries to extract patterns from time series
 Write your own classification, clustering, or evolutionary algorithm
 Perform relative performance tuning and evaluation of Spark
 Master probabilistic models for sequential data
 Experiment with advanced techniques such as regularization and kernelization
 Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
 Apply key learning strategies to a technical analysis of financial markets
Publisher resources
Table of contents

Scala for Machine Learning
 Table of Contents
 Scala for Machine Learning
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Getting Started
 2. Hello World!
 3. Data Preprocessing

4. Unsupervised Learning

Clustering
 Kmeans clustering
 The expectationmaximization algorithm
 Dimension reduction
 Performance considerations
 Summary

Clustering
 5. Naïve Bayes Classifiers
 6. Regression and Regularization
 7. Sequential Data Models
 8. Kernel Models and Support Vector Machines

9. Artificial Neural Networks
 Feedforward neural networks
 The multilayer perceptron
 Evaluation
 Convolution neural networks
 Benefits and limitations
 Summary
 10. Genetic Algorithms
 11. Reinforcement Learning
 12. Scalable Frameworks
 A. Basic Concepts
 Index
Product information
 Title: Scala for Machine Learning
 Author(s):
 Release date: December 2014
 Publisher(s): Packt Publishing
 ISBN: 9781783558742
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