Scala for machine learning : data processing, ML algorithms, smart analytics, and more [E-Book] / Patrick R. Nicolas.
Nicolas, Patrick R., (author)
2nd edition
Birmingham : Packt Publishing, 2017
740 pages (online resource)
Full Text
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245 1 0 |a Scala for machine learning :  |b data processing, ML algorithms, smart analytics, and more  |h [E-Book] /  |c Patrick R. Nicolas. 
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505 0 |a Scala for machine learning : data processing, ML algorithms, smart analytics, and more, second edition -- Credits -- About the Author -- About the Reviewers -- -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started -- Chapter 2: Data Pipelines -- Chapter 3: Data Preprocessing -- Chapter 4: Unsupervised Learning -- Chapter 5: Dimension Reduction -- Chapter 6: Naïve Bayes Classifiers -- Chapter 7: Sequential Data Models -- Chapter 8: Monte Carlo Inference -- Chapter 9: Regression and Regularization -- Chapter 10: Multilayer Perceptron -- Chapter 11: Deep Learning -- Chapter 12: Kernel Models and SVM -- Chapter 13: Evolutionary Computing -- Chapter 14: Multiarmed Bandits -- Chapter 15: Reinforcement Learning -- Chapter 16: Parallelism in Scala and Akka -- Chapter 17: Apache Spark MLlib -- Appendix A: Basic Concepts -- Appendix B: References. 
520 3 |a 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. 
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650 0 |a Machine learning 
650 0 |a Regression analysis 
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