Description 
1 online resource (290 pages) 
Contents 
Cover; Title Page; Copyright and Credits; Contributors; About Packt; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; Solving problems with machine learning; Applied machine learning workflow; Data and problem definition; Measurement scales; Data collection; Finding or observing data; Generating data; Sampling traps; Data preprocessing; Data cleaning; Filling missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Finding similar items; Euclidean distances; NonEuclidean distances 

Chapter 2: Java Libraries and Platforms for Machine LearningThe need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout; Apache Spark; Deeplearning4j; MALLET; The Encog Machine Learning Framework; ELKI; MOA; Comparing libraries; Building a machine learning application; Traditional machine learning architecture; Dealing with big data; Big data application architecture; Summary; Chapter 3: Basic Algorithms  Classification, Regression, and Clustering; Before you start; Classification; Data; Loading data; Feature selection; Learning algorithms; Classifying new data 

Chapter 4: Customer Relationship Prediction with EnsemblesThe customer relationship database; Challenge; Dataset; Evaluation; Basic Naive Bayes classifier baseline; Getting the data; Loading the data; Basic modeling; Evaluating models; Implementing the Naive Bayes baseline; Advanced modeling with ensembles; Before we start; Data preprocessing; Attribute selection; Model selection; Performance evaluation; Ensemble methods  MOA; Summary; Chapter 5: Affinity Analysis; Market basket analysis; Affinity analysis; Association rule learning; Basic concepts; Database of transactions; Itemset and rule 

Evaluation and prediction error metricsThe confusion matrix; Choosing a classification algorithm; Classification using Encog; Classification using massive online analysis; Evaluation; Baseline classifiers; Decision tree; Lazy learning; Active learning; Regression; Loading the data; Analyzing attributes; Building and evaluating the regression model; Linear regression; Linear regression using Encog; Regression using MOA; Regression trees; Tips to avoid common regression problems; Clustering; Clustering algorithms; Evaluation; Clustering using Encog; Clustering using ELKI; Summary 

The curse of dimensionalityClustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Precision and recall; Roc curves; Regression; Linear regression; Logistic regression; Evaluating regression; Mean squared error; Mean absolute error; Correlation coefficient; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Crossvalidation; Leaveoneout validation; Stratification; Summary 
Summary 
Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering 
Notes 
Support 

Print version record 
Subject 
Application software  Development.


Java (Computer program language)


Machine learning  Development


Application software  Development.


COMPUTERS  General.


Java (Computer program language)

Form 
Electronic book

Author 
Kaluza, Bostjan

ISBN 
1788473892 

9781788473897 
