Machine Learning in Java · 2nd Edition by Kaluza, Bostjan -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning in Java Second Edition

Contributors

About the authors About the reviewer Packt is searching for authors like you

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

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 Non-Euclidean distances The curse of dimensionality

Clustering

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 Cross-validation Leave-one-out validation Stratification

Summary

Java Libraries and Platforms for Machine Learning

The 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

Basic Algorithms - Classification, Regression, and Clustering

Before you start Classification

Data Loading data Feature selection Learning algorithms Classifying new data Evaluation and prediction error metrics The 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

Customer Relationship Prediction with Ensembles

The 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

Affinity Analysis

Market basket analysis

Affinity analysis

Association rule learning

Basic concepts

Database of transactions Itemset and rule Support Lift Confidence

Apriori algorithm FP-Growth algorithm

The supermarket dataset Discover patterns

Apriori FP-Growth

Other applications in various areas

Medical diagnosis Protein sequences Census data Customer relationship management IT operations analytics

Summary

Recommendation Engines with Apache Mahout

Basic concepts

Key concepts User-based and item-based analysis Calculating similarity

Collaborative filtering Content-based filtering Hybrid approach

Exploitation versus exploration

Getting Apache Mahout

Configuring Mahout in Eclipse with the Maven plugin

Building a recommendation engine

Book ratings dataset Loading the data

Loading data from a file Loading data from a database In-memory databases

Collaborative filtering

User-based filtering Item-based filtering Adding custom rules to recommendations Evaluation Online learning engine

Content-based filtering Summary

Fraud and Anomaly Detection

Suspicious and anomalous behavior detection

Unknown unknowns

Suspicious pattern detection Anomalous pattern detection

Analysis types

Pattern analysis

Transaction analysis Plan recognition

Outlier detection using ELKI

An example using ELKI

Fraud detection in insurance claims

Dataset Modeling suspicious patterns

The vanilla approach Dataset rebalancing

Anomaly detection in website traffic

Dataset Anomaly detection in time series data

Using Encog for time series Histogram-based anomaly detection Loading the data Creating histograms Density-based k-nearest neighbors

Summary

Image Recognition with Deeplearning4j

Introducing image recognition

Neural networks

Perceptron Feedforward neural networks Autoencoder Restricted Boltzmann machine Deep convolutional networks

Image classification

Deeplearning4j

Getting DL4J

MNIST dataset Loading the data Building models

Building a single-layer regression model Building a deep belief network Building a multilayer convolutional network

Summary

Activity Recognition with Mobile Phone Sensors

Introducing activity recognition

Mobile phone sensors Activity recognition pipeline The plan

Collecting data from a mobile phone

Installing Android Studio Loading the data collector

Feature extraction

Collecting training data

Building a classifier

Reducing spurious transitions Plugging the classifier into a mobile app

Summary

Text Mining with Mallet - Topic Modeling and Spam Detection

Introducing text mining

Topic modeling Text classification

Installing Mallet Working with text data

Importing data

Importing from directory Importing from file

Pre-processing text data

Topic modeling for BBC News

BBC dataset Modeling Evaluating a model Reusing a model

Saving a model Restoring a model

Detecting email spam 

Email spam dataset Feature generation Training and testing

Model performance

Summary

What Is Next?

Machine learning in real life

Noisy data Class unbalance Feature selection Model chaining The importance of evaluation Getting models into production Model maintenance

Standards and markup languages

CRISP-DM SEMMA methodology Predictive model markup language

Machine learning in the cloud

Machine learning as a service

Web resources and competitions

Datasets Online courses Competitions Websites and blogs Venues and conferences

Summary

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