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  • Book Overview & Buying Scala for Machine Learning
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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
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Index
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Index

Linear regression

Linear regression is by far the most widely used, or at least the most commonly known, regression method. The terminology is usually associated with the concept of fitting a model to data. Linear regression can be implemented using the least squares method. Practically, the least squares method entails the minimization of the sum of the squares of the error between the observed data and the actual model.

The least squares problems fall into two categories:

  • Ordinary least squares
  • Nonlinear least squares

One-variate linear regression

Let's start with the simplest form of linear regression, which is the single variable regression, in order to introduce the terms and concepts behind linear regression. In its simplest interpretation, the one-variate linear regression consists of fitting a line to a set of data points {x, y}.

Note

Single variable linear regression is given by the following formula:

One-variate linear regression

Here, w1 is the slope, w0 is the intercept, f is the linear function that minimizes...

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Programming languages
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Scala for Machine Learning
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