Scala for Machine Learning

Leverage Scala and Machine Learning to construct and study systems that can learn from data
Preview in Mapt

Scala for Machine Learning

Patrick R. Nicolas

1 customer reviews
Leverage Scala and Machine Learning to construct and study systems that can learn from data
Mapt Subscription
FREE
$29.99/m after trial
eBook
$25.20
RRP $35.99
Save 29%
Print + eBook
$59.99
RRP $59.99
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$25.20
$59.99
$29.99p/m after trial
RRP $35.99
RRP $59.99
Subscription
eBook
Print + eBook
Start 30 Day Trial

Frequently bought together


Scala for Machine Learning Book Cover
Scala for Machine Learning
$ 35.99
$ 25.20
Scala for Machine Learning - Second Edition Book Cover
Scala for Machine Learning - Second Edition
$ 47.99
$ 33.60
Buy 2 for $35.00
Save $48.98
Add to Cart
Subscribe and access every Packt eBook & Video.
 
  • 5,000+ eBooks & Videos
  • 50+ New titles a month
  • 1 Free eBook/Video to keep every month
Start Free Trial
 

Book Details

ISBN 139781783558742
Paperback520 pages

Book Description

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering designs, biometrics, and trading strategies, to detection of genetic anomalies.

The book begins with an introduction to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.

Next, you'll learn about data preprocessing and filtering techniques. Following this, you'll move on to clustering and dimension reduction, Naïve Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.

Table of Contents

Chapter 1: Getting Started
Mathematical notation for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Don't reinvent the wheel!
Tools and frameworks
Source code
Let's kick the tires
Summary
Chapter 2: Hello World!
Modeling
Defining a methodology
Monadic data transformation
A workflow computational model
Profiling data
Assessing a model
Summary
Chapter 3: Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Summary
Chapter 4: Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Summary
Chapter 5: Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
The Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
Chapter 6: Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Summary
Chapter 7: Sequential Data Models
Markov decision processes
The hidden Markov model
Conditional random fields
Regularized CRFs and text analytics
Comparing CRF and HMM
Performance consideration
Summary
Chapter 8: Kernel Models and Support Vector Machines
Kernel functions
Support vector machines
Support vector classifiers – SVC
Anomaly detection with one-class SVC
Support vector regression
Performance considerations
Summary
Chapter 9: Artificial Neural Networks
Feed-forward neural networks
The multilayer perceptron
Evaluation
Convolution neural networks
Benefits and limitations
Summary
Chapter 10: Genetic Algorithms
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
Chapter 11: Reinforcement Learning
Reinforcement learning
Learning classifier systems
Summary
Chapter 12: Scalable Frameworks
An overview
Scala
Scalability with Actors
Akka
Apache Spark
Summary

What You Will Learn

  • Build dynamic workflows for scientific computing
  • Leverage open source libraries to extract patterns from time series
  • Write your own classification, clustering, or evolutionary algorithm
  • Perform relative performance tuning and evaluation of Spark
  • Master probabilistic models for sequential data
  • Experiment with advanced techniques such as regularization and kernelization
  • Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
  • Apply key learning strategies to a technical analysis of financial markets

Authors

Table of Contents

Chapter 1: Getting Started
Mathematical notation for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Don't reinvent the wheel!
Tools and frameworks
Source code
Let's kick the tires
Summary
Chapter 2: Hello World!
Modeling
Defining a methodology
Monadic data transformation
A workflow computational model
Profiling data
Assessing a model
Summary
Chapter 3: Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Summary
Chapter 4: Unsupervised Learning
Clustering
Dimension reduction
Performance considerations
Summary
Chapter 5: Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
The Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
Chapter 6: Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Summary
Chapter 7: Sequential Data Models
Markov decision processes
The hidden Markov model
Conditional random fields
Regularized CRFs and text analytics
Comparing CRF and HMM
Performance consideration
Summary
Chapter 8: Kernel Models and Support Vector Machines
Kernel functions
Support vector machines
Support vector classifiers – SVC
Anomaly detection with one-class SVC
Support vector regression
Performance considerations
Summary
Chapter 9: Artificial Neural Networks
Feed-forward neural networks
The multilayer perceptron
Evaluation
Convolution neural networks
Benefits and limitations
Summary
Chapter 10: Genetic Algorithms
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
Chapter 11: Reinforcement Learning
Reinforcement learning
Learning classifier systems
Summary
Chapter 12: Scalable Frameworks
An overview
Scala
Scalability with Actors
Akka
Apache Spark
Summary

Book Details

ISBN 139781783558742
Paperback520 pages
Read More
From 1 reviews

Read More Reviews

Recommended for You

Building Machine Learning Systems with Python Book Cover
Building Machine Learning Systems with Python
$ 29.99
$ 6.00
Machine Learning with Spark Book Cover
Machine Learning with Spark
$ 29.99
$ 3.00
Practical Data Analysis Book Cover
Practical Data Analysis
$ 29.99
$ 21.00
Python Machine Learning Book Cover
Python Machine Learning
$ 35.99
$ 25.20
Practical Data Science Cookbook Book Cover
Practical Data Science Cookbook
$ 29.99
$ 21.00
Machine Learning with R Book Cover
Machine Learning with R
$ 32.99
$ 23.10