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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
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
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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

Pros and cons

The examples selected in this chapter do not do justice to the versatility and accuracy of the Naïve Bayes family of classifiers.

Naïve Bayes classifiers are simple and robust generative classifiers that rely on prior conditional probabilities to extract a model from a training dataset. The Naïve Bayes has its benefits, as mentioned here:

  • Simple implementation and easy to parallelize
  • Very low computational complexity: O((n+c)*m), where m is the number of features, C the number of classes, and n the number of observations
  • Handles missing data
  • Supports incremental updates, insertions, and deletions

However, Naïve Bayes is not a silver bullet. It has the following disadvantages:

  • The assumption of the independence of features is not practical in the real world
  • It requires a large training set to achieve reasonable accuracy
  • It contains a zero-frequency problem for counters

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