Scala for Machine Learning

More Information
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
About

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving 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 re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.

Features
  • Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and source code
  • Leverage your expertise in Scala programming to create and customize AI applications with your own scalable machine learning algorithms
  • Experiment with different techniques, and evaluate their benefits and limitations using real-world financial applications, in a tutorial style
Page Count 520
Course Length 15 hours 36 minutes
ISBN 9781783558742
Date Of Publication 18 Dec 2015

Authors

Patrick R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.