Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala

Take your expertise in Scala programming to the next level by creating and customizing AI applications

Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style

Book Description

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 design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies.

The book is your one stop guide that introduces you 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. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naive Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You'll move on to evolutionary computing, multibandit algorithms, and reinforcement learning.

Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.

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

Dive into neural networks and some deep learning architecture

Apply some basic multiarm-bandit algorithms

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 Author

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.

Table of Contents

Getting Started

Data pipeline

Data pre-processing

Clustering

Dimension reduction

Naive Bayes Classifiers

Sequential data models

Monte Carlo Inference

Regression and Regularization

Multi-layer perceptron

Deep learning

Kernel models & support vector machines

Evolutionary computing

Multi-arm bandits

Reinforcement learning

Parallelism in Scala and Akka

Apache Spark

Appendix Basic concepts

References

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