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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

Summary

This concludes the description and implementation of linear and logistic regression and the concept of regularization to reduce overfitting. Your first analytical projects using machine learning will (or did) likely involve a regression model of some type. Regression models, along with the Naïve Bayes classification, are the most understood techniques for those without a deep knowledge of statistics or machine learning.

At the completion of this chapter, you hopefully have a grasp on the following:

The concept of linear and nonlinear least squares-based optimization
The implementation of ordinary least square regression as well as logistic regression
The impact of regularization with an implementation of the Ridge regression

The logistic regression is also the foundation of the conditional random fields introduced in the next chapter and artificial neural networks in Chapter 9, Artificial Neural Networks.

Contrary to the Naïve Bayes models (refer to Chapter 5, Naïve Bayes...

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Programming languages

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