Machine Learning in Java

Title Page
Copyright and Credits
Contributors
About Packt
Table of Contents
Preface
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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Index