更新时间：2021-08-05 17:05:06
封面
版权页
Credits
About the Author
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 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
Chapter 2. Hello World!
Modeling
Designing a workflow
Assessing a model
Chapter 3. Data Preprocessing
Time series
Moving averages
Fourier analysis
The Kalman filter
Alternative preprocessing techniques
Chapter 4. Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Chapter 5. Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Chapter 6. Regression and Regularization
Linear regression
Regularization
Numerical optimization
The logistic regression
Chapter 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
Chapter 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)
Chapter 9. Artificial Neural Networks
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
Chapter 10. Genetic Algorithms
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Chapter 11. Reinforcement Learning
Introduction
Learning classifier systems
Chapter 12. Scalable Frameworks
Overview
Scala
Scalability with Actors
Akka
Apache Spark
Appendix A. Basic Concepts
Scala programming
Mathematics
Finances 101
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