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.
Scala for Machine Learning, Second Edition - Second Edition
Scala for Machine Learning, Second Edition - Second Edition
Overview of this book
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 Naïve 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.
Table of Contents (27 chapters)
Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started
Data Pipelines
Data Preprocessing
Unsupervised Learning
Dimension Reduction
Naïve Bayes Classifiers
Sequential Data Models
Monte Carlo Inference
Regression and Regularization
Multilayer Perceptron
Deep Learning
Kernel Models and SVM
Evolutionary Computing
Multiarmed Bandits
Reinforcement Learning
Parallelism in Scala and Akka
Apache Spark MLlib
Basic Concepts
References
Index
Customer Reviews