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Scala for Machine Learning

Scala for Machine Learning

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

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!
Table of Contents (15 chapters)
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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
1. Getting Started
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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
2. Hello World!
2. Hello World!
Modeling
Designing a workflow
Assessing a model
Summary
3
3. Data Preprocessing
3. Data Preprocessing
Time series
Moving averages
Fourier analysis
The Kalman filter
Alternative preprocessing techniques
Summary
4
4. Unsupervised Learning
4. Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Summary
5
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
6. Regression and Regularization
6. Regression and Regularization
Linear regression
Regularization
Numerical optimization
The logistic regression
Summary
7
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
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
9. Artificial Neural Networks
9. Artificial Neural Networks
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
Summary
10
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
11. Reinforcement Learning
11. Reinforcement Learning
Introduction
Learning classifier systems
Summary
12
12. Scalable Frameworks
12. Scalable Frameworks
Overview
Scala
Scalability with Actors
Akka
Apache Spark
Summary
13
A. Basic Concepts
A. Basic Concepts
Scala programming
Mathematics
Finances 101
Suggested online courses
References
14
Index
Index

Chapter 1. Getting Started

It is critical for any computer scientist to understand the different classes of machine learning algorithms and be able to select the ones that are relevant to the domain of their expertise and dataset. However, the application of these algorithms represents a small fraction of the overall effort needed to extract an accurate and performing model from input data. A common data mining workflow consists of the following sequential steps:

  1. Loading the data.
  2. Preprocessing, analyzing, and filtering the input data.
  3. Discovering patterns, affinities, clusters, and classes.
  4. Selecting the model features and the appropriate machine learning algorithm(s).
  5. Refining and validating the model.
  6. Improving the computational performance of the implementation.

As we will emphasize throughout this book, each stage of the process is critical to build the right model.

This first chapter introduces you to the taxonomy of machine learning algorithms, the tools and frameworks used in the book, and a simple application of logistic regression to get your feet wet.

End of Section 1
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