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  • Book Overview & Buying Scala for Machine Learning
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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
2
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
4
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
6
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
7
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
9
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
11
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
12
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
13
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

Naïve Bayes classifiers

This conditional independence between X features is an essential requirement for the Naïve Bayes classifier. It also restricts its applicability. The Naïve Bayes classification is better understood through simple, concrete examples [5:5].

Introducing the multinomial Naïve Bayes

Let's consider the problem of how to predict change in interest rates. The first step is to list the factors that potentially may trigger or cause an increase or decrease in the interest rates. For the sake of illustrating Naïve Bayes, we will select the consumer price index (CPI), change in the Federal fund rate (FDF) and the gross domestic product (GDP) as a first set of features. The terminology is described in the Terminology section under Finances 101 in Appendix A, Basic Concepts.

The use case is to predict direction of the change in the yield of the 1-year Treasury bill (1yTB), taking into account the change in the current CPI, FDF, and GDP. The objective...

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Programming languages
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Scala for Machine Learning
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