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Scala for Machine Learning

Scala for Machine Learning

By : R. Nicolas
3.8 (12)
Buy this Book
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Scala for Machine Learning

Scala for Machine Learning

3.8 (12)
By: R. Nicolas
Buy this Book

Overview of this book

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!
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started
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1. Getting Started
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Mathematical notation for the curious
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Why machine learning?
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Why Scala?
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Model categorization
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Taxonomy of machine learning algorithms
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Tools and frameworks
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Source code
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Let's kick the tires
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Summary
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2. Hello World!
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2. Hello World!
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Modeling
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Designing a workflow
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Assessing a model
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Summary
3
3. Data Preprocessing
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3. Data Preprocessing
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Time series
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Moving averages
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Fourier analysis
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The Kalman filter
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Alternative preprocessing techniques
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Summary
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4. Unsupervised Learning
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4. Unsupervised Learning
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Clustering
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Dimension reduction
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Performance considerations
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Summary
5
5. Naïve Bayes Classifiers
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5. Naïve Bayes Classifiers
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Probabilistic graphical models
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Naïve Bayes classifiers
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Multivariate Bernoulli classification
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Naïve Bayes and text mining
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Pros and cons
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Summary
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6. Regression and Regularization
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6. Regression and Regularization
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Linear regression
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Regularization
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Numerical optimization
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The logistic regression
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Summary
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7. Sequential Data Models
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7. Sequential Data Models
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Markov decision processes
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The hidden Markov model (HMM)
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Conditional random fields
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CRF and text analytics
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Comparing CRF and HMM
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Performance consideration
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Summary
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8. Kernel Models and Support Vector Machines
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8. Kernel Models and Support Vector Machines
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Kernel functions
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The support vector machine (SVM)
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Support vector classifier (SVC)
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Anomaly detection with one-class SVC
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Support vector regression (SVR)
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Performance considerations
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Summary
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9. Artificial Neural Networks
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9. Artificial Neural Networks
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Feed-forward neural networks (FFNN)
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The multilayer perceptron (MLP)
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Evaluation
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Benefits and limitations
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Summary
10
10. Genetic Algorithms
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10. Genetic Algorithms
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Evolution
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Genetic algorithms and machine learning
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Genetic algorithm components
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Implementation
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GA for trading strategies
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Advantages and risks of genetic algorithms
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Summary
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11. Reinforcement Learning
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11. Reinforcement Learning
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Introduction
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Learning classifier systems
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Summary
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12. Scalable Frameworks
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12. Scalable Frameworks
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Overview
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Scala
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Scalability with Actors
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Akka
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Apache Spark
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Summary
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A. Basic Concepts
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A. Basic Concepts
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Scala programming
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Mathematics
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Finances 101
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Suggested online courses
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References
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Index
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Index

Performance considerations

The three unsupervised learning techniques share the same limitation—a high computational complexity.

K-means

The K-means has the computational complexity of O(iKnm), where i is the number of iterations, K the number of clusters, n the number of observations, and m the number of features. The algorithm can be improved through the use of other techniques by using the following techniques:

  • Reducing the average number of iterations by seeding the centroid using an algorithm such as initialization by ranking the variance of the initial cluster as described at the beginning of this chapter.
  • Using a parallel implementation of K-means and leveraging a large-scale framework such as Hadoop or Spark.
  • Reducing the number of outliers and possible features by filtering out the noise with a smoothing algorithm such as a discrete Fourier transform or a Kalman filter.
  • Decreasing the dimensions of the model by following a two-step process: a first pass with a smaller number of...

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