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Scala for Machine Learning

By : R. Nicolas

3.8 (12)

Scala for Machine Learning

3.8 (12)

By: R. Nicolas

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!

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started

1. Getting Started

Mathematical notation for the curious

Why machine learning?

Why Scala?

Model categorization

Taxonomy of machine learning algorithms

Tools and frameworks

Source code

Let's kick the tires

Summary

2. Hello World!

2. Hello World!

Modeling

Designing a workflow

Assessing a model

Summary

3. Data Preprocessing

3. Data Preprocessing

Time series

Moving averages

Fourier analysis

The Kalman filter

Alternative preprocessing techniques

Summary

4. Unsupervised Learning

4. Unsupervised Learning

Clustering

Dimension reduction

Performance considerations

Summary

5. Naïve Bayes Classifiers

5. Naïve Bayes Classifiers

Probabilistic graphical models

Naïve Bayes classifiers

Multivariate Bernoulli classification

Naïve Bayes and text mining

Pros and cons

Summary

6. Regression and Regularization

6. Regression and Regularization

Linear regression

Regularization

Numerical optimization

The logistic regression

Summary

7. Sequential Data Models

7. Sequential Data Models

Markov decision processes

The hidden Markov model (HMM)

Conditional random fields

CRF and text analytics

Comparing CRF and HMM

Performance consideration

Summary

8. Kernel Models and Support Vector Machines

8. Kernel Models and Support Vector Machines

Kernel functions

The support vector machine (SVM)

Support vector classifier (SVC)

Anomaly detection with one-class SVC

Support vector regression (SVR)

Performance considerations

Summary

9. Artificial Neural Networks

9. Artificial Neural Networks

Feed-forward neural networks (FFNN)

The multilayer perceptron (MLP)

Evaluation

Benefits and limitations

Summary

10. Genetic Algorithms

10. Genetic Algorithms

Evolution

Genetic algorithms and machine learning

Genetic algorithm components

Implementation

GA for trading strategies

Advantages and risks of genetic algorithms

Summary

11. Reinforcement Learning

11. Reinforcement Learning

Introduction

Learning classifier systems

Summary

12. Scalable Frameworks

12. Scalable Frameworks

Overview

Scala

Scalability with Actors

Akka

Apache Spark

Summary

A. Basic Concepts

A. Basic Concepts

Scala programming

Mathematics

Finances 101

Suggested online courses

References

Index

Index

Customer Reviews

3.8 (12)

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1 star

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Support vector classifier (SVC)

Support vector machines can be applied to classification, anomalies detection, and regression problems. Let's dive into the support vector classifiers first.

The binary SVC

The first classifier to be evaluated is the binary (2-class) support vector classifier. The implementation uses the LIBSVM library created by Chih-Chung Chang and Chih-Jen Lin from the National Taiwan University [8:9].

LIBSVM

The library was originally written in C and ported to Java. It can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm as a .zip or tar.gzip file. The library includes the following classifier modes:

Support vector classifiers (C-SVC, υ-SVC, and one-class SVC)
Support vector regression (υ-SVR and ε-SVR)
RBF, linear, sigmoid, polynomial, and precomputed kernels

LIBSVM has the distinct advantage of using Sequential Minimal Optimization (SMO), which reduces the time complexity of a training of n observations to O(n²). LIBSVM documentation covers...

Visually different images

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Tech Concepts

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Programming languages

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