Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Scala for Machine Learning
  • Table Of Contents Toc
  • Feedback & Rating feedback
Scala for Machine Learning

Scala for Machine Learning

By : R. Nicolas
3.8 (12)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Free Chapter
1
1. Getting Started
Icon
1. Getting Started
Icon
Mathematical notation for the curious
Icon
Why machine learning?
Icon
Why Scala?
Icon
Model categorization
Icon
Taxonomy of machine learning algorithms
Icon
Tools and frameworks
Icon
Source code
Icon
Let's kick the tires
Icon
Summary
2
2. Hello World!
Icon
2. Hello World!
Icon
Modeling
Icon
Designing a workflow
Icon
Assessing a model
Icon
Summary
3
3. Data Preprocessing
Icon
3. Data Preprocessing
Icon
Time series
Icon
Moving averages
Icon
Fourier analysis
Icon
The Kalman filter
Icon
Alternative preprocessing techniques
Icon
Summary
4
4. Unsupervised Learning
Icon
4. Unsupervised Learning
Icon
Clustering
Icon
Dimension reduction
Icon
Performance considerations
Icon
Summary
5
5. Naïve Bayes Classifiers
Icon
5. Naïve Bayes Classifiers
Icon
Probabilistic graphical models
Icon
Naïve Bayes classifiers
Icon
Multivariate Bernoulli classification
Icon
Naïve Bayes and text mining
Icon
Pros and cons
Icon
Summary
6
6. Regression and Regularization
Icon
6. Regression and Regularization
Icon
Linear regression
Icon
Regularization
Icon
Numerical optimization
Icon
The logistic regression
Icon
Summary
7
7. Sequential Data Models
Icon
7. Sequential Data Models
Icon
Markov decision processes
Icon
The hidden Markov model (HMM)
Icon
Conditional random fields
Icon
CRF and text analytics
Icon
Comparing CRF and HMM
Icon
Performance consideration
Icon
Summary
8
8. Kernel Models and Support Vector Machines
chevron up
Icon
8. Kernel Models and Support Vector Machines
Icon
Kernel functions
Icon
The support vector machine (SVM)
Icon
Support vector classifier (SVC)
Icon
Anomaly detection with one-class SVC
Icon
Support vector regression (SVR)
Icon
Performance considerations
Icon
Summary
9
9. Artificial Neural Networks
Icon
9. Artificial Neural Networks
Icon
Feed-forward neural networks (FFNN)
Icon
The multilayer perceptron (MLP)
Icon
Evaluation
Icon
Benefits and limitations
Icon
Summary
10
10. Genetic Algorithms
Icon
10. Genetic Algorithms
Icon
Evolution
Icon
Genetic algorithms and machine learning
Icon
Genetic algorithm components
Icon
Implementation
Icon
GA for trading strategies
Icon
Advantages and risks of genetic algorithms
Icon
Summary
11
11. Reinforcement Learning
Icon
11. Reinforcement Learning
Icon
Introduction
Icon
Learning classifier systems
Icon
Summary
12
12. Scalable Frameworks
Icon
12. Scalable Frameworks
Icon
Overview
Icon
Scala
Icon
Scalability with Actors
Icon
Akka
Icon
Apache Spark
Icon
Summary
13
A. Basic Concepts
Icon
A. Basic Concepts
Icon
Scala programming
Icon
Mathematics
Icon
Finances 101
Icon
Suggested online courses
Icon
References
14
Index
Icon
Index

Kernel functions

Every machine learning model introduced in this book so far assumes that observations are represented by a feature vector of a fixed size. However, some real-world applications such as text mining or genomics do not lend themselves to this restriction. The critical element of the process of classification is to define a similarity or a distance between two observations. Kernel functions allow developers to compute the similarity between observations without the need to encode them in feature vectors [8:1].

Overview

The concept of kernel methods may be a bit odd at first to a novice. It is usually better understood by using a concrete example. Let's consider the example of the classification of proteins. Proteins have different lengths and composition, but it does not prevent scientists from classifying them [8:2].

Note

Proteins:

Proteins are polymers of amino acids joined together by peptide bonds. They are composed of a carbon atom bonded to a hydrogen atom, another...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 2
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY