Scala for Machine Learning Front Cover

Scala for Machine Learning

  • Length: 420 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2014-12-31
  • ISBN-10: 1783558741
  • ISBN-13: 9781783558742
  • Sales Rank: #1937773 (See Top 100 Books)

Leverage Scala and Machine Learning to construct and study systems that can learn from data

About This Book

  • 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

Who This Book Is For

Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

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 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, Naive 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.

Table of Contents

Chapter 1. Getting Started
Chapter 2. Hello World!
Chapter 3. Data Preprocessing
Chapter 4. Unsupervised Learning
Chapter 5. Naïve Bayes Classifiers
Chapter 6. Regression and Regularization
Chapter 7. Sequential Data Models
Chapter 8. Kernel Models and Support Vector Machin
Chapter 9. Artificial Neural Networks
Chapter 10. Genetic Algorithms
Chapter 11. Reinforcement Learning
Chapter 12. Scalable Frameworks

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