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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
14
Index
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Index

Markov decision processes

This first section also describes the basic concepts you need to know in order to understand, develop, and apply the hidden Markov model. The foundation of the Markovian universe is the concept known as the Markov property.

The Markov property

The Markov property is a characteristic of a stochastic process where the conditional probability distribution of a future state depends on the current state and not on its past states. In this case, the transition between the states occurs at a discrete time, and the Markov property is known as the discrete Markov chain.

The first-order discrete Markov chain

The following example is taken from Introduction to Machine Learning by E. Alpaydin [7:2].

Let's consider the following use case. N balls of different colors are hidden in N boxes (one each). The balls can have only three colors {Blue, Red, and Green}. The experimenter draws the balls one by one. The state of the discovery process is defined by the color of latest ball...

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Programming languages
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Scala for Machine Learning
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